Black Brands and Moral Affirmation
Jonathan Doriscar
2023-10-22
0. Beginning stuff
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options(scipen = 100)wf_dt <- read_csv("~/Documents/White Fragility/Buying Black and Moral Affirmation/wf_maS1.csv")## Rows: 327 Columns: 116
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (39): StartDate, EndDate, RecordedDate, ResponseId, DistributionChannel,...
## dbl (76): ParticipantID, Status, Progress, Duration (in seconds), Finished, ...
## num (1): race
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## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.View(wf_dt)
attach(wf_dt)colnames(wf_dt)## [1] "ParticipantID" "StartDate" "EndDate"
## [4] "Status" "Progress" "Duration (in seconds)"
## [7] "Finished" "RecordedDate" "ResponseId"
## [10] "DistributionChannel" "UserLanguage" "consent"
## [13] "prolificID" "BA_1" "BA_2"
## [16] "BA_3" "BA_4" "SE1"
## [19] "BMIS_sad_1" "BMIS_shame_1" "BMIS_guilt_1"
## [22] "BMIS_tired_1" "BMIS_nervous_1" "BMIS_calm_1"
## [25] "BMIS_fedup_1" "BMIS_loving_1" "BMIS_angry_1"
## [28] "BMIS_lively_1" "BMIS_caring_1" "BMIS_content_1"
## [31] "BMIS_gloomy_1" "BMIS_jittery_1" "BMIS_drowsy_1"
## [34] "BMIS_happy_1" "Q1 RP1" "Q2 RP2"
## [37] "Q3 RP3" "Q4 RP4" "Q5 RP5"
## [40] "Q6 RP6" "Q7 RP7" "Q8 RN1"
## [43] "Q9 RN2" "Q10 RN3" "Q11 RN4"
## [46] "Q12 RN5" "Q13 RN6" "Q14 RN7"
## [49] "Q15 LP1" "Q16 LP2" "Q17 LP3"
## [52] "Q18 LP4" "Q19 LP5" "Q20 LP6"
## [55] "Q21 LP7" "Q22 LN1" "Q23 LN2"
## [58] "Q24 LN3" "Q25 LN4" "Q26 LN5"
## [61] "Q27 LN6" "Q28 LN7" "spwtime_First Click"
## [64] "spwtime_Last Click" "spwtime_Page Submit" "spwtime_Click Count"
## [67] "attentioncheck_nb" "discrepancy_nb" "BMIS_sad_2"
## [70] "BMIS_shame_2" "BMIS_guilt_2" "BMIS_tired_2"
## [73] "BMIS_nervous_2" "BMIS_calm_2" "BMIS_fedup_2"
## [76] "BMIS_loving_2" "BMIS_angry_2" "BMIS_lively_2"
## [79] "BMIS_caring_2" "BMIS_content_2" "BMIS_gloomy_2"
## [82] "BMIS_jittery_2" "BMIS_drowsy_2" "BMIS_happy_2"
## [85] "credibility" "objective" "valid"
## [88] "useful" "rl_product_choice" "rl_shop_intentions"
## [91] "rl_purchase" "rl_wom" "rr_product_choice"
## [94] "rr_shop_intentions" "rr_purchase" "rr_wom"
## [97] "attentioncheck_b" "discrepancy_b" "SE_2"
## [100] "age" "race" "education"
## [103] "polit_affil" "polit_affil_4_TEXT" "polit_affil_cont_1"
## [106] "gender" "gender_4_TEXT" "iat_prev"
## [109] "iat_racial" "iat_racial_time" "iat_racial_quant"
## [112] "recent_results" "nobias_white" "nobias_black"
## [115] "bias_white" "bias_black"summary(wf_dt[,19:34])## BMIS_sad_1 BMIS_shame_1 BMIS_guilt_1 BMIS_tired_1 BMIS_nervous_1
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.00 Min. :1.000
## 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:2.00 1st Qu.:1.000
## Median :2.000 Median :1.000 Median :1.000 Median :3.00 Median :2.000
## Mean :1.923 Mean :1.611 Mean :1.675 Mean :2.71 Mean :1.871
## 3rd Qu.:3.000 3rd Qu.:2.000 3rd Qu.:2.000 3rd Qu.:3.00 3rd Qu.:3.000
## Max. :4.000 Max. :4.000 Max. :4.000 Max. :4.00 Max. :4.000
## NA's :16 NA's :16 NA's :16 NA's :17 NA's :16
## BMIS_calm_1 BMIS_fedup_1 BMIS_loving_1 BMIS_angry_1 BMIS_lively_1
## Min. :1.000 Min. :1.00 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:3.000 1st Qu.:1.00 1st Qu.:2.000 1st Qu.:1.000 1st Qu.:2.000
## Median :3.000 Median :1.00 Median :3.000 Median :1.000 Median :2.000
## Mean :3.132 Mean :1.81 Mean :2.728 Mean :1.437 Mean :2.259
## 3rd Qu.:4.000 3rd Qu.:3.00 3rd Qu.:3.000 3rd Qu.:2.000 3rd Qu.:3.000
## Max. :4.000 Max. :4.00 Max. :4.000 Max. :4.000 Max. :4.000
## NA's :16 NA's :16 NA's :18 NA's :16 NA's :18
## BMIS_caring_1 BMIS_content_1 BMIS_gloomy_1 BMIS_jittery_1
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:1.000 1st Qu.:1.000
## Median :3.000 Median :3.000 Median :2.000 Median :1.000
## Mean :2.929 Mean :2.842 Mean :1.897 Mean :1.605
## 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:3.000 3rd Qu.:2.000
## Max. :4.000 Max. :4.000 Max. :4.000 Max. :4.000
## NA's :16 NA's :16 NA's :16 NA's :16
## BMIS_drowsy_1 BMIS_happy_1
## Min. :1.000 Min. :1.000
## 1st Qu.:1.000 1st Qu.:2.000
## Median :2.000 Median :3.000
## Mean :2.129 Mean :2.768
## 3rd Qu.:3.000 3rd Qu.:3.000
## Max. :4.000 Max. :4.000
## NA's :17 NA's :16summary(wf_dt[,69:84])## BMIS_sad_2 BMIS_shame_2 BMIS_guilt_2 BMIS_tired_2
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:2.000
## Median :2.000 Median :1.000 Median :1.000 Median :3.000
## Mean :1.986 Mean :1.776 Mean :1.763 Mean :2.642
## 3rd Qu.:3.000 3rd Qu.:3.000 3rd Qu.:3.000 3rd Qu.:3.000
## Max. :4.000 Max. :4.000 Max. :4.000 Max. :4.000
## NA's :32 NA's :33 NA's :32 NA's :34
## BMIS_nervous_2 BMIS_calm_2 BMIS_fedup_2 BMIS_loving_2
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:1.000 1st Qu.:3.000 1st Qu.:1.000 1st Qu.:2.000
## Median :2.000 Median :3.000 Median :1.000 Median :3.000
## Mean :1.844 Mean :2.963 Mean :1.687 Mean :2.693
## 3rd Qu.:3.000 3rd Qu.:4.000 3rd Qu.:2.000 3rd Qu.:3.000
## Max. :4.000 Max. :4.000 Max. :4.000 Max. :4.000
## NA's :33 NA's :32 NA's :33 NA's :34
## BMIS_angry_2 BMIS_lively_2 BMIS_caring_2 BMIS_content_2
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:1.000 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000
## Median :1.000 Median :2.000 Median :3.000 Median :3.000
## Mean :1.485 Mean :2.278 Mean :2.827 Mean :2.803
## 3rd Qu.:2.000 3rd Qu.:3.000 3rd Qu.:4.000 3rd Qu.:3.500
## Max. :4.000 Max. :4.000 Max. :4.000 Max. :4.000
## NA's :32 NA's :32 NA's :33 NA's :32
## BMIS_gloomy_2 BMIS_jittery_2 BMIS_drowsy_2 BMIS_happy_2
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:2.000
## Median :2.000 Median :1.000 Median :2.000 Median :3.000
## Mean :1.898 Mean :1.646 Mean :2.156 Mean :2.617
## 3rd Qu.:3.000 3rd Qu.:2.000 3rd Qu.:3.000 3rd Qu.:3.000
## Max. :4.000 Max. :4.000 Max. :4.000 Max. :4.000
## NA's :33 NA's :33 NA's :32 NA's :32summary(wf_dt[,14:17])## BA_1 BA_2 BA_3 BA_4
## Min. :1.000 Min. :1.000 Min. :1.00 Min. :1.000
## 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.00 1st Qu.:2.000
## Median :3.000 Median :3.000 Median :4.00 Median :4.000
## Mean :3.334 Mean :3.273 Mean :3.73 Mean :3.987
## 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.00 3rd Qu.:5.500
## Max. :7.000 Max. :7.000 Max. :7.00 Max. :7.000
## NA's :16 NA's :16 NA's :16 NA's :16wf_dt$discrepancy_nb <- car::recode(wf_dt$discrepancy_nb, "1 = '3'; 2 = '2'; 3 = '1'; 4 = '0'; 5 = '-1'; 6 = '-2'; 7 = '-3'")
wf_dt$attentioncheck_nb <- car::recode(wf_dt$attentioncheck_nb, "1 = '3'; 2 = '2'; 3 = '1'; 4 = '0'; 5 = '-1'; 6 = '-2'; 7 = '-3'")
wf_dt$discrepancy_b <- car::recode(wf_dt$discrepancy_b, "1 = '3'; 2 = '2'; 3 = '1'; 4 = '0'; 5 = '-1'; 6 = '-2'; 7 = '-3'")
wf_dt$attentioncheck_b <- car::recode(wf_dt$attentioncheck_b, "1 = '3'; 2 = '2'; 3 = '1'; 4 = '0'; 5 = '-1'; 6 = '-2'; 7 = '-3'") # larger scores indicate greater changes
wf_dt$discrepancy_bias <- (wf_dt$discrepancy_b - wf_dt$attentioncheck_b) # deal with this later
wf_dt$discrepancy_nobias <- (wf_dt$discrepancy_nb + wf_dt$discrepancy_nb) # deal with this later
wf_dt$bias_discrepancy <- coalesce(wf_dt$discrepancy_bias, wf_dt$discrepancy_nobias) wf_dt$attentioncheck_nb <- as.numeric(wf_dt$attentioncheck_nb)
is.numeric(wf_dt$attentioncheck_nb)## [1] TRUEwhich(wf_dt$attentioncheck_nb == "1", arr.ind = TRUE) # seeing which people failed my attention checks## [1] 36 116 179 212 277 283 310 318 319which(wf_dt$attentioncheck_nb == "2", arr.ind = TRUE)## [1] 77 115 208 240 247 249which(wf_dt$attentioncheck_nb == "3", arr.ind = TRUE)## [1] 21 167 192 325which(wf_dt$attentioncheck_nb == "5", arr.ind = TRUE)## integer(0)which(wf_dt$attentioncheck_nb == "6", arr.ind = TRUE)## integer(0)which(wf_dt$attentioncheck_nb == "7", arr.ind = TRUE)## integer(0)wf_dt$attentioncheck_b <- as.numeric(wf_dt$attentioncheck_b)
is.numeric(wf_dt$attentioncheck_b)## [1] TRUEwhich(wf_dt$attentioncheck_b == "4", arr.ind = TRUE) # seeing which people failed my attention checks## integer(0)which(wf_dt$attentioncheck_b == "2", arr.ind = TRUE)## [1] 2 26 69 134 200 201 251 261 272which(wf_dt$attentioncheck_b == "3", arr.ind = TRUE)## [1] 5 9 11 17 20 24 29 30 34 35 37 38 40 43 44 45 48 49
## [19] 50 52 56 58 62 63 64 73 75 80 83 86 87 91 92 93 94 97
## [37] 98 102 104 105 107 108 112 122 124 125 128 129 131 135 136 137 139 140
## [55] 143 144 146 147 149 150 151 154 157 160 161 163 164 176 180 182 183 184
## [73] 185 186 187 188 189 190 193 195 196 198 202 205 226 231 232 233 236 238
## [91] 239 245 248 252 253 255 257 258 259 263 266 268 270 273 275 276 278 279
## [109] 281 282 285 286 289 293 294 296 297 300 304 305 306 311 312 313 316 317
## [127] 320 321 322 324 326 327which(wf_dt$attentioncheck_b == "5", arr.ind = TRUE)## integer(0)which(wf_dt$attentioncheck_b == "6", arr.ind = TRUE)## integer(0)which(wf_dt$attentioncheck_b == "7", arr.ind = TRUE)## integer(0)wf_dt2 <- wf_dt[-c(61, 81, 88, 82, 15, 301, 315, 323, 254),] # only lost 9 peoplewf_dt2$bias_discrepancy_centered <- scale(wf_dt2$bias_discrepancy, center = TRUE, scale = FALSE) # Centering
wf_dt2$bias_discrepancy_Z <- scale(wf_dt2$bias_discrepancy, center = TRUE, scale = TRUE) # Z-scorewf_dt2 <- wf_dt2 %>%
mutate(
condition = rowSums(select(., nobias_white, nobias_black, bias_white, bias_black), na.rm = TRUE)
)
as.factor(wf_dt2$condition)## [1] 0 3 0 1 2 0 1 0 2 1 3 1 0 1 0 3 1 0 2 1 1 0 3 1 2 0 1 3 3 0 1 3 2 3 0 3 2
## [38] 0 3 1 0 2 2 3 0 0 2 2 2 0 3 0 1 1 3 1 3 3 0 2 3 2 0 1 0 1 2 0 2 0 2 0 3 1
## [75] 1 3 1 2 2 0 0 2 3 2 0 2 3 3 3 1 1 3 2 1 1 0 3 0 3 3 1 3 3 0 0 1 3 0 0 1 1
## [112] 0 2 1 0 2 2 0 2 3 0 1 3 3 0 3 1 0 2 3 2 2 1 2 2 1 1 2 2 0 2 3 0 3 2 2 1 0
## [149] 3 0 3 3 1 0 3 2 0 2 2 0 1 0 1 1 0 1 0 0 1 1 3 1 0 0 2 1 3 2 2 2 2 3 2 3 3
## [186] 0 1 3 1 3 2 0 3 0 3 2 2 3 2 3 0 3 1 1 0 0 1 0 0 0 0 3 1 1 2 1 0 0 3 2 2 2
## [223] 1 0 0 2 3 2 0 1 3 1 3 2 0 0 1 2 0 3 0 1 2 1 0 2 3 3 2 1 3 2 3 0 2 1 3 0 1
## [260] 3 0 2 1 2 0 3 2 1 3 2 0 2 3 1 2 3 0 1 2 3 1 0 2 1 0 0 2 3 0 3 2 0 1 2 0 0
## [297] 2 3 3 0 1 0 1 3 3 2 1 2 3 1 1 2 2 3 2 1 2 3
## Levels: 0 1 2 3wf_dt2 <- wf_dt2 %>%
mutate(
bias = case_when(
condition %in% c(0, 1) ~ 0,
condition %in% c(2, 3) ~ 1,
TRUE ~ NA_integer_
),
brand_race = case_when(
condition %in% c(0, 2) ~ 0,
condition %in% c(1, 3) ~ 1,
TRUE ~ NA_integer_
)
)wf_dt2 <- wf_dt2 %>%
mutate(
shop_intentions = rowSums(select(., rl_shop_intentions, rr_shop_intentions), na.rm = TRUE)
)
# Centering and z-score for "shop_intentions"
wf_dt2$shop_intentions_centered <- scale(wf_dt2$shop_intentions, center = TRUE, scale = FALSE) # Centering
wf_dt2$shop_intentions_Z <- scale(wf_dt2$shop_intentions, center = TRUE, scale = TRUE) # Z-scorewf_dt2 <- wf_dt2 %>%
mutate(
purchase = rowSums(select(., rl_purchase, rr_purchase), na.rm = TRUE)
)
# Centering and z-score for "purchase"
wf_dt2$purchase_centered <- scale(wf_dt2$purchase, center = TRUE, scale = FALSE) # Centering
wf_dt2$purchase_Z <- scale(wf_dt2$purchase, center = TRUE, scale = TRUE) # Z-scorewf_dt2 <- wf_dt2 %>%
mutate(
wom = rowSums(select(., rl_wom, rr_wom), na.rm = TRUE)
)
# Centering and z-score for "wom"
wf_dt2$wom_centered <- scale(wf_dt2$wom, center = TRUE, scale = FALSE) # Centering
wf_dt2$wom_Z <- scale(wf_dt2$wom, center = TRUE, scale = TRUE) # Z-score** Demographics**
race_proportions <- table(wf_dt2$race)/length(wf_dt2$race) # creating race table
(race_percentages <- race_proportions*100) # multiplying the table by 100 to get percentages##
## 1 3 4 6 7 14 15
## 86.7924528 0.3144654 0.3144654 0.3144654 0.6289308 0.3144654 0.3144654
## 16
## 0.9433962gender_proportions <- table(wf_dt2$gender)/length(wf_dt2$gender) # creating gender table
(gender_percentages <- gender_proportions*100) # multiplying the table by 100 to get percentages##
## 1 2 3 4 5
## 41.8238994 45.2830189 2.2012579 0.3144654 0.3144654political_proportions <- table(wf_dt2$polit_affil)/length(wf_dt2$polit_affil) # creating political table
(political_percentages <- political_proportions*100) # multiplying the table by 100 to get percentages##
## 1 2 3 4 5
## 17.610063 47.169811 22.012579 1.886792 1.257862education_proportions <- table(wf_dt2$education)/length(wf_dt2$education) # creating education table
(education_percentages <- education_proportions*100) # multiplying the table by 100 to get percentages##
## 1 2 3 4 5 6 7
## 0.6289308 10.6918239 18.8679245 9.7484277 36.4779874 12.8930818 0.6289308iatprev_proportions <- table(wf_dt2$iat_prev)/length(wf_dt2$iat_prev) # creating IAT previously table
(iatprev_percentages <- iatprev_proportions*100) # multiplying the table by 100 to get percentages##
## 21 22
## 59.74843 30.18868iatracial_proportions <- table(wf_dt2$iat_racial)/length(wf_dt2$iat_racial) # creating IAT racial table
(iatracial_percentages <- iatracial_proportions*100) # multiplying the table by 100 to get percentages##
## 21 22
## 6.289308 23.8993711. Defensiveness
hildebrand.rule(credibility, na.rm = TRUE)## Group Ratio Skew
## 1 xx -0.115851 No Skewhildebrand.rule(objective, na.rm = TRUE)## Group Ratio Skew
## 1 xx 0.008063028 No Skewhildebrand.rule(valid, na.rm = TRUE)## Group Ratio Skew
## 1 xx -0.1385317 No Skewhildebrand.rule(useful, na.rm = TRUE)## Group Ratio Skew
## 1 xx -0.2647419 Negative Skewsd(credibility, na.rm = TRUE)## [1] 1.708679sd(objective, na.rm = TRUE)## [1] 1.698944sd(valid, na.rm = TRUE)## [1] 1.705757sd(useful, na.rm = TRUE)## [1] 1.759273hist(credibility)
hist(objective)
hist(valid)
hist(useful)
defensive <- select(wf_dt2, 85:88)
psych::alpha(defensive)##
## Reliability analysis
## Call: psych::alpha(x = defensive)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.92 0.92 0.91 0.74 11 0.0078 3.8 1.6 0.73
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.9 0.92 0.93
## Duhachek 0.9 0.92 0.93
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## credibility 0.87 0.87 0.84 0.70 6.9 0.0126 0.0144 0.67
## objective 0.94 0.94 0.91 0.83 14.7 0.0063 0.0022 0.83
## valid 0.86 0.86 0.82 0.68 6.3 0.0135 0.0094 0.65
## useful 0.89 0.89 0.87 0.73 8.3 0.0109 0.0155 0.67
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## credibility 284 0.93 0.93 0.91 0.86 3.8 1.7
## objective 283 0.81 0.82 0.70 0.68 4.0 1.7
## valid 283 0.94 0.94 0.94 0.90 3.8 1.7
## useful 283 0.90 0.90 0.85 0.81 3.5 1.8
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 7 miss
## credibility 0.15 0.11 0.10 0.27 0.23 0.08 0.06 0.11
## objective 0.13 0.06 0.12 0.30 0.17 0.14 0.07 0.11
## valid 0.15 0.10 0.17 0.23 0.19 0.11 0.05 0.11
## useful 0.18 0.15 0.15 0.21 0.19 0.07 0.06 0.11wf_dt2$defensive <- rowMeans(wf_dt2[,85:88]) # creating defensiveness variable
myscale <- 1:7 #defining scale to reverse defensivness variable
wf_dt2 <- wf_dt2 %>%
mutate(defensive_reverse = min(myscale) - defensive + max(myscale)) # reversing defensivness scale
wf_dt2$defensive_reverse_centered <- scale(wf_dt2$defensive_reverse, center = TRUE, scale = FALSE) #centering the variable
wf_dt2$defensive_reverse_Z <- scale(wf_dt2$defensive_reverse, center = TRUE, scale = TRUE) # z score defensiveness (def_r_mean = mean(wf_dt2$defensive_reverse, na.rm = TRUE)) # mean defensivness## [1] 4.241906(def_r_sd = sd(wf_dt2$defensive_reverse, na.rm = TRUE)) # sd defensiveness## [1] 1.548895hist(wf_dt2$defensive_reverse_Z) # histogram of defensive z scores
# Perform ANOVA for defensive_reverse by bias
summary((anova_defensive_reverse <- aov(defensive_reverse ~ as.factor(bias)*as.factor(brand_race), data = wf_dt2)))## Df Sum Sq Mean Sq F value Pr(>F)
## as.factor(bias) 1 56.5 56.49 25.670 0.000000746
## as.factor(brand_race) 1 0.5 0.53 0.243 0.622
## as.factor(bias):as.factor(brand_race) 1 4.5 4.50 2.047 0.154
## Residuals 274 603.0 2.20
##
## as.factor(bias) ***
## as.factor(brand_race)
## as.factor(bias):as.factor(brand_race)
## Residuals
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 40 observations deleted due to missingness# Get summary of ANOVA
(tukey_posthoc <- TukeyHSD(anova_defensive_reverse))## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = defensive_reverse ~ as.factor(bias) * as.factor(brand_race), data = wf_dt2)
##
## $`as.factor(bias)`
## diff lwr upr p adj
## 1-0 0.9044271 0.5530046 1.25585 0.0000007
##
## $`as.factor(brand_race)`
## diff lwr upr p adj
## 1-0 0.08766191 -0.2626675 0.4379914 0.6226813
##
## $`as.factor(bias):as.factor(brand_race)`
## diff lwr upr p adj
## 1:0-0:0 0.6523987 -0.00004964437 1.3048470 0.0500256
## 0:1-0:0 -0.1879277 -0.86613489865 0.4902796 0.8905901
## 1:1-0:0 0.9755824 0.31120396399 1.6399609 0.0010360
## 0:1-1:0 -0.8403263 -1.48166470937 -0.1989880 0.0044803
## 1:1-1:0 0.3231838 -0.30351282130 0.9498803 0.5427063
## 1:1-0:1 1.1635101 0.51003871844 1.8169815 0.00003782. Bias Awareness
hildebrand.rule(BA_1, na.rm = TRUE)## Group Ratio Skew
## 1 xx 0.1849701 No Skewhildebrand.rule(BA_2, na.rm = TRUE)## Group Ratio Skew
## 1 xx 0.1529581 No Skewhildebrand.rule(BA_3, na.rm = TRUE)## Group Ratio Skew
## 1 xx -0.148806 No Skewhildebrand.rule(BA_4, na.rm = TRUE)## Group Ratio Skew
## 1 xx -0.006890947 No Skewsd(BA_1, na.rm = TRUE)## [1] 1.807888sd(BA_2, na.rm = TRUE)## [1] 1.786841sd(BA_3, na.rm = TRUE)## [1] 1.815091sd(BA_4, na.rm = TRUE)## [1] 1.866469hist(BA_1)
hist(BA_2)
hist(BA_3)
hist(BA_4)
bias_aware <- select(wf_dt2, 14:17)
psych::alpha(bias_aware)##
## Reliability analysis
## Call: psych::alpha(x = bias_aware)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.82 0.82 0.79 0.53 4.5 0.017 3.6 1.5 0.54
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.78 0.82 0.85
## Duhachek 0.78 0.82 0.85
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## BA_1 0.81 0.81 0.75 0.59 4.3 0.019 0.0084 0.55
## BA_2 0.75 0.75 0.69 0.50 3.0 0.024 0.0123 0.55
## BA_3 0.71 0.71 0.63 0.45 2.5 0.028 0.0053 0.46
## BA_4 0.80 0.80 0.75 0.58 4.1 0.019 0.0141 0.58
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## BA_1 302 0.75 0.75 0.61 0.55 3.3 1.8
## BA_2 302 0.83 0.83 0.76 0.68 3.3 1.8
## BA_3 302 0.88 0.88 0.85 0.76 3.7 1.8
## BA_4 302 0.77 0.76 0.63 0.57 4.0 1.9
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 7 miss
## BA_1 0.19 0.25 0.13 0.06 0.24 0.11 0.02 0.05
## BA_2 0.20 0.26 0.13 0.06 0.25 0.08 0.03 0.05
## BA_3 0.15 0.19 0.12 0.09 0.30 0.12 0.04 0.05
## BA_4 0.12 0.18 0.12 0.09 0.25 0.18 0.07 0.05wf_dt2$bias_aware <- rowMeans(wf_dt2[,14:17])
wf_dt2$bias_aware_center <- scale(wf_dt2$bias_aware, center = TRUE, scale = FALSE) #centering the variable
wf_dt2$bias_aware_Z <- scale(wf_dt2$bias_aware, center = TRUE, scale = TRUE) #Z scores (BA_mean = mean(wf_dt2$bias_aware, na.rm = TRUE)) # mean of bias awareness## [1] 3.563742(BA_sd = sd(wf_dt2$bias_aware, na.rm = TRUE)) # sd of bias awareness## [1] 1.463663hist(wf_dt2$bias_aware_Z)
# Perform ANOVA for bias_aware by condition
summary((anova_bias_aware <- aov(bias_aware ~ as.factor(condition), data = wf_dt2)))## Df Sum Sq Mean Sq F value Pr(>F)
## as.factor(condition) 3 5.1 1.714 0.798 0.496
## Residuals 298 639.7 2.147
## 16 observations deleted due to missingness# Run Tukey post hoc test
(tukey_posthoc <- TukeyHSD(anova_bias_aware))## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = bias_aware ~ as.factor(condition), data = wf_dt2)
##
## $`as.factor(condition)`
## diff lwr upr p adj
## 1-0 -0.29681467 -0.9279424 0.3343131 0.6177784
## 2-0 0.01830357 -0.6012124 0.6378196 0.9998402
## 3-0 -0.18177656 -0.8049880 0.4414349 0.8750921
## 2-1 0.31511824 -0.2954025 0.9256390 0.5423101
## 3-1 0.11503812 -0.4992322 0.7293085 0.9626422
## 3-2 -0.20008013 -0.8024138 0.4022536 0.82631523. Emotions
hildebrand.rule(BMIS_lively_1, na.rm = TRUE)## Group Ratio Skew
## 1 xx 0.290114 Positive Skewhildebrand.rule(BMIS_lively_2, na.rm = TRUE)## Group Ratio Skew
## 1 xx 0.3121008 Positive Skewhildebrand.rule(BMIS_happy_1, na.rm = TRUE)## Group Ratio Skew
## 1 xx -0.2571164 Negative Skewhildebrand.rule(BMIS_happy_2, na.rm = TRUE)## Group Ratio Skew
## 1 xx -0.4123413 Negative Skewhildebrand.rule(BMIS_sad_1, na.rm = TRUE)## Group Ratio Skew
## 1 xx -0.0779093 No Skewhildebrand.rule(BMIS_sad_2, na.rm = TRUE)## Group Ratio Skew
## 1 xx -0.01324874 No Skewhildebrand.rule(BMIS_shame_1, na.rm = TRUE)## Group Ratio Skew
## 1 xx 0.7093866 Positive Skewhildebrand.rule(BMIS_shame_2, na.rm = TRUE)## Group Ratio Skew
## 1 xx 0.803145 Positive Skewhildebrand.rule(BMIS_guilt_1, na.rm = TRUE)## Group Ratio Skew
## 1 xx 0.7735714 Positive Skewhildebrand.rule(BMIS_guilt_2, na.rm = TRUE)## Group Ratio Skew
## 1 xx 0.7938481 Positive Skewhildebrand.rule(BMIS_tired_1, na.rm = TRUE)## Group Ratio Skew
## 1 xx -0.299749 Negative Skewhildebrand.rule(BMIS_tired_2, na.rm = TRUE)## Group Ratio Skew
## 1 xx -0.3492858 Negative Skewhildebrand.rule(BMIS_caring_1, na.rm = TRUE)## Group Ratio Skew
## 1 xx -0.07747474 No Skewhildebrand.rule(BMIS_caring_2, na.rm = TRUE)## Group Ratio Skew
## 1 xx -0.1813282 No Skewhildebrand.rule(BMIS_content_1, na.rm = TRUE)## Group Ratio Skew
## 1 xx -0.1684255 No Skewhildebrand.rule(BMIS_content_2, na.rm = TRUE)## Group Ratio Skew
## 1 xx -0.2072466 Negative Skewhildebrand.rule(BMIS_gloomy_1, na.rm = TRUE)## Group Ratio Skew
## 1 xx -0.1031091 No Skewhildebrand.rule(BMIS_gloomy_2, na.rm = TRUE)## Group Ratio Skew
## 1 xx -0.09969695 No Skewhildebrand.rule(BMIS_jittery_1, na.rm = TRUE)## Group Ratio Skew
## 1 xx 0.7373568 Positive Skewhildebrand.rule(BMIS_jittery_2, na.rm = TRUE)## Group Ratio Skew
## 1 xx 0.7611705 Positive Skewhildebrand.rule(BMIS_drowsy_1, na.rm = TRUE)## Group Ratio Skew
## 1 xx 0.1266293 No Skewhildebrand.rule(BMIS_drowsy_2, na.rm = TRUE)## Group Ratio Skew
## 1 xx 0.1536428 No Skewhildebrand.rule(BMIS_nervous_1, na.rm = TRUE)## Group Ratio Skew
## 1 xx -0.1351261 No Skewhildebrand.rule(BMIS_nervous_2, na.rm = TRUE)## Group Ratio Skew
## 1 xx -0.1633531 No Skewhildebrand.rule(BMIS_calm_1, na.rm = TRUE)## Group Ratio Skew
## 1 xx 0.1559281 No Skewhildebrand.rule(BMIS_calm_2, na.rm = TRUE)## Group Ratio Skew
## 1 xx -0.04192267 No Skewhildebrand.rule(BMIS_jittery_1, na.rm = TRUE)## Group Ratio Skew
## 1 xx 0.7373568 Positive Skewhildebrand.rule(BMIS_jittery_2, na.rm = TRUE)## Group Ratio Skew
## 1 xx 0.7611705 Positive Skewhildebrand.rule(BMIS_loving_1, na.rm = TRUE)## Group Ratio Skew
## 1 xx -0.2786378 Negative Skewhildebrand.rule(BMIS_loving_2, na.rm = TRUE)## Group Ratio Skew
## 1 xx -0.3080362 Negative Skewhildebrand.rule(BMIS_fedup_1, na.rm = TRUE)## Group Ratio Skew
## 1 xx 0.8267106 Positive Skewhildebrand.rule(BMIS_fedup_2, na.rm = TRUE)## Group Ratio Skew
## 1 xx 0.7907416 Positive Skewsd(BMIS_lively_1, na.rm = TRUE)## [1] 0.8924067sd(BMIS_lively_2, na.rm = TRUE)## [1] 0.8906292sd(BMIS_happy_1, na.rm = TRUE)## [1] 0.9004142sd(BMIS_happy_2, na.rm = TRUE)## [1] 0.9289656sd(BMIS_sad_1, na.rm = TRUE)## [1] 0.9905162sd(BMIS_sad_2, na.rm = TRUE)## [1] 1.023443sd(BMIS_shame_1, na.rm = TRUE)## [1] 0.8612123sd(BMIS_shame_2, na.rm = TRUE)## [1] 0.9655918sd(BMIS_guilt_1, na.rm = TRUE)## [1] 0.872888sd(BMIS_guilt_2, na.rm = TRUE)## [1] 0.9607782sd(BMIS_tired_1, na.rm = TRUE)## [1] 0.9685524sd(BMIS_tired_2, na.rm = TRUE)## [1] 1.025984sd(BMIS_caring_1, na.rm = TRUE)## [1] 0.913066sd(BMIS_caring_2, na.rm = TRUE)## [1] 0.9566599sd(BMIS_content_1, na.rm = TRUE)## [1] 0.9354656sd(BMIS_content_2, na.rm = TRUE)## [1] 0.9486772sd(BMIS_gloomy_1, na.rm = TRUE)## [1] 0.997913sd(BMIS_gloomy_2, na.rm = TRUE)## [1] 1.02351sd(BMIS_jittery_1, na.rm = TRUE)## [1] 0.8198224sd(BMIS_jittery_2, na.rm = TRUE)## [1] 0.8490325sd(BMIS_drowsy_1, na.rm = TRUE)## [1] 1.018976sd(BMIS_drowsy_2, na.rm = TRUE)## [1] 1.014901sd(BMIS_nervous_1, na.rm = TRUE)## [1] 0.9518324sd(BMIS_nervous_2, na.rm = TRUE)## [1] 0.9578181sd(BMIS_calm_1, na.rm = TRUE)## [1] 0.8454715sd(BMIS_calm_2, na.rm = TRUE)## [1] 0.8894503sd(BMIS_jittery_1, na.rm = TRUE)## [1] 0.8198224sd(BMIS_jittery_2, na.rm = TRUE)## [1] 0.8490325sd(BMIS_loving_1, na.rm = TRUE)## [1] 0.9756201sd(BMIS_loving_2, na.rm = TRUE)## [1] 0.9971792sd(BMIS_fedup_1, na.rm = TRUE)## [1] 0.9801367sd(BMIS_fedup_2, na.rm = TRUE)## [1] 0.8688993hist(BMIS_lively_1, na.rm = TRUE)## Warning in plot.window(xlim, ylim, "", ...): "na.rm" is not a graphical
## parameter## Warning in title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...):
## "na.rm" is not a graphical parameter## Warning in axis(1, ...): "na.rm" is not a graphical parameter## Warning in axis(2, at = yt, ...): "na.rm" is not a graphical parameter
hist(BMIS_lively_2, na.rm = TRUE)## Warning in plot.window(xlim, ylim, "", ...): "na.rm" is not a graphical
## parameter## Warning in title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...):
## "na.rm" is not a graphical parameter## Warning in axis(1, ...): "na.rm" is not a graphical parameter## Warning in axis(2, at = yt, ...): "na.rm" is not a graphical parameter
hist(BMIS_happy_1, na.rm = TRUE)## Warning in plot.window(xlim, ylim, "", ...): "na.rm" is not a graphical
## parameter## Warning in title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...):
## "na.rm" is not a graphical parameter## Warning in axis(1, ...): "na.rm" is not a graphical parameter## Warning in axis(2, at = yt, ...): "na.rm" is not a graphical parameter
hist(BMIS_happy_2, na.rm = TRUE)## Warning in plot.window(xlim, ylim, "", ...): "na.rm" is not a graphical
## parameter## Warning in title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...):
## "na.rm" is not a graphical parameter## Warning in axis(1, ...): "na.rm" is not a graphical parameter## Warning in axis(2, at = yt, ...): "na.rm" is not a graphical parameter
hist(BMIS_sad_1, na.rm = TRUE)## Warning in plot.window(xlim, ylim, "", ...): "na.rm" is not a graphical
## parameter## Warning in title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...):
## "na.rm" is not a graphical parameter## Warning in axis(1, ...): "na.rm" is not a graphical parameter## Warning in axis(2, at = yt, ...): "na.rm" is not a graphical parameter
hist(BMIS_sad_2, na.rm = TRUE)## Warning in plot.window(xlim, ylim, "", ...): "na.rm" is not a graphical
## parameter## Warning in title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...):
## "na.rm" is not a graphical parameter## Warning in axis(1, ...): "na.rm" is not a graphical parameter## Warning in axis(2, at = yt, ...): "na.rm" is not a graphical parameter
hist(BMIS_shame_1, na.rm = TRUE)## Warning in plot.window(xlim, ylim, "", ...): "na.rm" is not a graphical
## parameter## Warning in title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...):
## "na.rm" is not a graphical parameter## Warning in axis(1, ...): "na.rm" is not a graphical parameter## Warning in axis(2, at = yt, ...): "na.rm" is not a graphical parameter
hist(BMIS_shame_2, na.rm = TRUE)## Warning in plot.window(xlim, ylim, "", ...): "na.rm" is not a graphical
## parameter## Warning in title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...):
## "na.rm" is not a graphical parameter## Warning in axis(1, ...): "na.rm" is not a graphical parameter## Warning in axis(2, at = yt, ...): "na.rm" is not a graphical parameter
hist(BMIS_guilt_1, na.rm = TRUE)## Warning in plot.window(xlim, ylim, "", ...): "na.rm" is not a graphical
## parameter## Warning in title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...):
## "na.rm" is not a graphical parameter## Warning in axis(1, ...): "na.rm" is not a graphical parameter## Warning in axis(2, at = yt, ...): "na.rm" is not a graphical parameter
hist(BMIS_guilt_2, na.rm = TRUE)## Warning in plot.window(xlim, ylim, "", ...): "na.rm" is not a graphical
## parameter## Warning in title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...):
## "na.rm" is not a graphical parameter## Warning in axis(1, ...): "na.rm" is not a graphical parameter## Warning in axis(2, at = yt, ...): "na.rm" is not a graphical parameter
hist(BMIS_tired_1, na.rm = TRUE)## Warning in plot.window(xlim, ylim, "", ...): "na.rm" is not a graphical
## parameter## Warning in title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...):
## "na.rm" is not a graphical parameter## Warning in axis(1, ...): "na.rm" is not a graphical parameter## Warning in axis(2, at = yt, ...): "na.rm" is not a graphical parameter
hist(BMIS_tired_2, na.rm = TRUE)## Warning in plot.window(xlim, ylim, "", ...): "na.rm" is not a graphical
## parameter## Warning in title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...):
## "na.rm" is not a graphical parameter## Warning in axis(1, ...): "na.rm" is not a graphical parameter## Warning in axis(2, at = yt, ...): "na.rm" is not a graphical parameter
hist(BMIS_caring_1, na.rm = TRUE)## Warning in plot.window(xlim, ylim, "", ...): "na.rm" is not a graphical
## parameter## Warning in title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...):
## "na.rm" is not a graphical parameter## Warning in axis(1, ...): "na.rm" is not a graphical parameter## Warning in axis(2, at = yt, ...): "na.rm" is not a graphical parameter
hist(BMIS_caring_2, na.rm = TRUE)## Warning in plot.window(xlim, ylim, "", ...): "na.rm" is not a graphical
## parameter## Warning in title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...):
## "na.rm" is not a graphical parameter## Warning in axis(1, ...): "na.rm" is not a graphical parameter## Warning in axis(2, at = yt, ...): "na.rm" is not a graphical parameter
hist(BMIS_content_1, na.rm = TRUE)## Warning in plot.window(xlim, ylim, "", ...): "na.rm" is not a graphical
## parameter## Warning in title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...):
## "na.rm" is not a graphical parameter## Warning in axis(1, ...): "na.rm" is not a graphical parameter## Warning in axis(2, at = yt, ...): "na.rm" is not a graphical parameter
hist(BMIS_content_2, na.rm = TRUE)## Warning in plot.window(xlim, ylim, "", ...): "na.rm" is not a graphical
## parameter## Warning in title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...):
## "na.rm" is not a graphical parameter## Warning in axis(1, ...): "na.rm" is not a graphical parameter## Warning in axis(2, at = yt, ...): "na.rm" is not a graphical parameter
hist(BMIS_gloomy_1, na.rm = TRUE)## Warning in plot.window(xlim, ylim, "", ...): "na.rm" is not a graphical
## parameter## Warning in title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...):
## "na.rm" is not a graphical parameter## Warning in axis(1, ...): "na.rm" is not a graphical parameter## Warning in axis(2, at = yt, ...): "na.rm" is not a graphical parameter
hist(BMIS_gloomy_2, na.rm = TRUE)## Warning in plot.window(xlim, ylim, "", ...): "na.rm" is not a graphical
## parameter## Warning in title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...):
## "na.rm" is not a graphical parameter## Warning in axis(1, ...): "na.rm" is not a graphical parameter## Warning in axis(2, at = yt, ...): "na.rm" is not a graphical parameter
hist(BMIS_jittery_1, na.rm = TRUE)## Warning in plot.window(xlim, ylim, "", ...): "na.rm" is not a graphical
## parameter## Warning in title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...):
## "na.rm" is not a graphical parameter## Warning in axis(1, ...): "na.rm" is not a graphical parameter## Warning in axis(2, at = yt, ...): "na.rm" is not a graphical parameter
hist(BMIS_jittery_2, na.rm = TRUE)## Warning in plot.window(xlim, ylim, "", ...): "na.rm" is not a graphical
## parameter## Warning in title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...):
## "na.rm" is not a graphical parameter## Warning in axis(1, ...): "na.rm" is not a graphical parameter## Warning in axis(2, at = yt, ...): "na.rm" is not a graphical parameter
hist(BMIS_drowsy_1, na.rm = TRUE)## Warning in plot.window(xlim, ylim, "", ...): "na.rm" is not a graphical
## parameter## Warning in title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...):
## "na.rm" is not a graphical parameter## Warning in axis(1, ...): "na.rm" is not a graphical parameter## Warning in axis(2, at = yt, ...): "na.rm" is not a graphical parameter
hist(BMIS_drowsy_2, na.rm = TRUE)## Warning in plot.window(xlim, ylim, "", ...): "na.rm" is not a graphical
## parameter## Warning in title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...):
## "na.rm" is not a graphical parameter## Warning in axis(1, ...): "na.rm" is not a graphical parameter## Warning in axis(2, at = yt, ...): "na.rm" is not a graphical parameter
hist(BMIS_nervous_1, na.rm = TRUE)## Warning in plot.window(xlim, ylim, "", ...): "na.rm" is not a graphical
## parameter## Warning in title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...):
## "na.rm" is not a graphical parameter## Warning in axis(1, ...): "na.rm" is not a graphical parameter## Warning in axis(2, at = yt, ...): "na.rm" is not a graphical parameter
hist(BMIS_nervous_2, na.rm = TRUE)## Warning in plot.window(xlim, ylim, "", ...): "na.rm" is not a graphical
## parameter## Warning in title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...):
## "na.rm" is not a graphical parameter## Warning in axis(1, ...): "na.rm" is not a graphical parameter## Warning in axis(2, at = yt, ...): "na.rm" is not a graphical parameter
hist(BMIS_calm_1, na.rm = TRUE)## Warning in plot.window(xlim, ylim, "", ...): "na.rm" is not a graphical
## parameter## Warning in title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...):
## "na.rm" is not a graphical parameter## Warning in axis(1, ...): "na.rm" is not a graphical parameter## Warning in axis(2, at = yt, ...): "na.rm" is not a graphical parameter
hist(BMIS_calm_2, na.rm = TRUE)## Warning in plot.window(xlim, ylim, "", ...): "na.rm" is not a graphical
## parameter## Warning in title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...):
## "na.rm" is not a graphical parameter## Warning in axis(1, ...): "na.rm" is not a graphical parameter## Warning in axis(2, at = yt, ...): "na.rm" is not a graphical parameter
hist(BMIS_jittery_1, na.rm = TRUE)## Warning in plot.window(xlim, ylim, "", ...): "na.rm" is not a graphical
## parameter## Warning in title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...):
## "na.rm" is not a graphical parameter## Warning in axis(1, ...): "na.rm" is not a graphical parameter## Warning in axis(2, at = yt, ...): "na.rm" is not a graphical parameter
hist(BMIS_jittery_2, na.rm = TRUE)## Warning in plot.window(xlim, ylim, "", ...): "na.rm" is not a graphical
## parameter## Warning in title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...):
## "na.rm" is not a graphical parameter## Warning in axis(1, ...): "na.rm" is not a graphical parameter## Warning in axis(2, at = yt, ...): "na.rm" is not a graphical parameter
hist(BMIS_loving_1, na.rm = TRUE)## Warning in plot.window(xlim, ylim, "", ...): "na.rm" is not a graphical
## parameter## Warning in title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...):
## "na.rm" is not a graphical parameter## Warning in axis(1, ...): "na.rm" is not a graphical parameter## Warning in axis(2, at = yt, ...): "na.rm" is not a graphical parameter
hist(BMIS_loving_2, na.rm = TRUE)## Warning in plot.window(xlim, ylim, "", ...): "na.rm" is not a graphical
## parameter## Warning in title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...):
## "na.rm" is not a graphical parameter## Warning in axis(1, ...): "na.rm" is not a graphical parameter## Warning in axis(2, at = yt, ...): "na.rm" is not a graphical parameter
hist(BMIS_fedup_1, na.rm = TRUE)## Warning in plot.window(xlim, ylim, "", ...): "na.rm" is not a graphical
## parameter## Warning in title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...):
## "na.rm" is not a graphical parameter## Warning in axis(1, ...): "na.rm" is not a graphical parameter## Warning in axis(2, at = yt, ...): "na.rm" is not a graphical parameter
hist(BMIS_fedup_2, na.rm = TRUE)## Warning in plot.window(xlim, ylim, "", ...): "na.rm" is not a graphical
## parameter## Warning in title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...):
## "na.rm" is not a graphical parameter## Warning in axis(1, ...): "na.rm" is not a graphical parameter## Warning in axis(2, at = yt, ...): "na.rm" is not a graphical parameter
wf_dt2$BMIS_guilt_1_center <- scale(wf_dt2$BMIS_guilt_1, center = TRUE, scale = FALSE) #centering the variable
wf_dt2$BMIS_guilt_1_Z <- scale(wf_dt2$BMIS_guilt_1, center = TRUE, scale = TRUE) #Z score
wf_dt2$BMIS_shame_1_center <- scale(wf_dt2$BMIS_shame_1, center = TRUE, scale = FALSE) #centering the variable
wf_dt2$BMIS_shame_1_Z <- scale(wf_dt2$BMIS_shame_1, center = TRUE, scale = TRUE) #Z score
wf_dt2$BMIS_sad_1_center <- scale(wf_dt2$BMIS_sad_1, center = TRUE, scale = FALSE) #centering the variable
wf_dt2$BMIS_sad_1_Z <- scale(wf_dt2$BMIS_sad_1, center = TRUE, scale = TRUE) #Z scorewf_dt2$BMIS_guilt_2_center <- scale(wf_dt2$BMIS_guilt_2, center = TRUE, scale = FALSE) #centering the variable
wf_dt2$BMIS_guilt_2_Z <- scale(wf_dt2$BMIS_guilt_2, center = TRUE, scale = TRUE) #Z score
wf_dt2$BMIS_shame_2_center <- scale(wf_dt2$BMIS_shame_2, center = TRUE, scale = FALSE) #centering the variable
wf_dt2$BMIS_shame_2_Z <- scale(wf_dt2$BMIS_shame_2, center = TRUE, scale = TRUE) #Z score
wf_dt2$BMIS_sad_2_center <- scale(wf_dt2$BMIS_sad_2, center = TRUE, scale = FALSE) #centering the variable
wf_dt2$BMIS_sad_2_Z <- scale(wf_dt2$BMIS_sad_2, center = TRUE, scale = TRUE) #Z score(guilty_mean = mean(wf_dt2$BMIS_guilt, na.rm = TRUE))## Warning: Unknown or uninitialised column: `BMIS_guilt`.## Warning in mean.default(wf_dt2$BMIS_guilt, na.rm = TRUE): argument is not
## numeric or logical: returning NA## [1] NA(guilty_sd = sd(wf_dt2$BMIS_guilt, na.rm = TRUE))## Warning: Unknown or uninitialised column: `BMIS_guilt`.## [1] NA(shame_mean = mean(wf_dt2$BMIS_shame, na.rm = TRUE))## Warning: Unknown or uninitialised column: `BMIS_shame`.## Warning in mean.default(wf_dt2$BMIS_shame, na.rm = TRUE): argument is not
## numeric or logical: returning NA## [1] NA(shame_sd = sd(wf_dt2$BMIS_shame, na.rm = TRUE))## Warning: Unknown or uninitialised column: `BMIS_shame`.## [1] NAguilt_shame_sad <- select(wf_dt2, BMIS_sad_2, BMIS_shame_2, BMIS_guilt_2) # dropping certain variables for the alpha
psych::alpha(guilt_shame_sad)##
## Reliability analysis
## Call: psych::alpha(x = guilt_shame_sad)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.9 0.9 0.9 0.76 9.5 0.01 1.8 0.9 0.68
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.88 0.9 0.92
## Duhachek 0.88 0.9 0.92
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## BMIS_sad_2 0.97 0.97 0.94 0.94 29.3 0.0037 NA 0.94
## BMIS_shame_2 0.80 0.80 0.66 0.66 4.0 0.0226 NA 0.66
## BMIS_guilt_2 0.81 0.81 0.68 0.68 4.2 0.0216 NA 0.68
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## BMIS_sad_2 286 0.86 0.85 0.69 0.68 2.0 1.02
## BMIS_shame_2 285 0.95 0.95 0.96 0.88 1.8 0.96
## BMIS_guilt_2 286 0.94 0.95 0.95 0.87 1.8 0.96
##
## Non missing response frequency for each item
## 1 2 3 4 miss
## BMIS_sad_2 0.44 0.22 0.25 0.09 0.1
## BMIS_shame_2 0.54 0.20 0.20 0.06 0.1
## BMIS_guilt_2 0.55 0.20 0.20 0.06 0.1wf_dt2$guilt_shame_sad <- rowMeans(guilt_shame_sad)
wf_dt2$guilt_shame_sad_center <- scale(wf_dt2$guilt_shame_sad, center = TRUE, scale = FALSE) #centering the variable
wf_dt2$guilt_shame_sad_Z <- scale(wf_dt2$guilt_shame_sad, center = TRUE, scale = TRUE) #Z scores wf_dt2 <- wf_dt2 %>% mutate(shame_discrepancy = BMIS_shame_2_Z - BMIS_shame_1_Z) # higher scores indicate an increase in shame, lower scores indicate decrease in shame# Independent samples t-test for BMIS_shame by condition
(t_test_BMIS_shame_2 <- t.test(BMIS_shame_2 ~ bias, var.equal = TRUE, data = wf_dt2))##
## Two Sample t-test
##
## data: BMIS_shame_2 by bias
## t = -4.6422, df = 283, p-value = 0.000005282
## alternative hypothesis: true difference in means between group 0 and group 1 is not equal to 0
## 95 percent confidence interval:
## -0.7306252 -0.2955185
## sample estimates:
## mean in group 0 mean in group 1
## 1.500000 2.013072# Calculating Cohen's d for BMIS_shame by condition
(cohen_d_BMIS_shame_2 <- cohensD(BMIS_shame_2 ~ bias, data = wf_dt2))## [1] 0.5514569# Calculating standard deviations of BMIS_shame by condition
(sd_by_condition <- aggregate(BMIS_shame_2 ~ bias, data = wf_dt2, FUN = sd))## bias BMIS_shame_2
## 1 0 0.7666722
## 2 1 1.0512332# Independent samples t-test for BMIS_shame by condition
(t_test_BMIS_shame_2 <- t.test(BMIS_shame_1 ~ bias, var.equal = TRUE, data = wf_dt2))##
## Two Sample t-test
##
## data: BMIS_shame_1 by bias
## t = 0.23429, df = 300, p-value = 0.8149
## alternative hypothesis: true difference in means between group 0 and group 1 is not equal to 0
## 95 percent confidence interval:
## -0.1710664 0.2173041
## sample estimates:
## mean in group 0 mean in group 1
## 1.618056 1.594937# Calculating Cohen's d for BMIS_shame by condition
(cohen_d_BMIS_shame_2 <- cohensD(BMIS_shame_1 ~ bias, data = wf_dt2))## [1] 0.02699276# Calculating standard deviations of BMIS_shame by condition
(sd_by_condition <- aggregate(BMIS_shame_1 ~ bias, data = wf_dt2, FUN = sd))## bias BMIS_shame_1
## 1 0 0.8609356
## 2 1 0.8524074# One-way ANOVA for SE_2 by bias
summary((anova_SE2 <- aov(SE_2 ~ as.factor(bias)*as.factor(brand_race), data = wf_dt2)))## Df Sum Sq Mean Sq F value Pr(>F)
## as.factor(bias) 1 1.5 1.4913 0.850 0.357
## as.factor(brand_race) 1 1.9 1.9410 1.107 0.294
## as.factor(bias):as.factor(brand_race) 1 0.5 0.5037 0.287 0.592
## Residuals 282 494.7 1.7541
## 32 observations deleted due to missingness# Tukey post hoc test
(tukey_SE2 <- TukeyHSD(anova_SE2))## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = SE_2 ~ as.factor(bias) * as.factor(brand_race), data = wf_dt2)
##
## $`as.factor(bias)`
## diff lwr upr p adj
## 1-0 -0.1447737 -0.453845 0.1642976 0.357299
##
## $`as.factor(brand_race)`
## diff lwr upr p adj
## 1-0 0.1646979 -0.1436244 0.4730201 0.2939405
##
## $`as.factor(bias):as.factor(brand_race)`
## diff lwr upr p adj
## 1:0-0:0 -0.22516026 -0.8024587 0.3521382 0.7448843
## 0:1-0:0 0.07472826 -0.5192969 0.6687534 0.9881077
## 1:1-0:0 0.01791667 -0.5645624 0.6003957 0.9998195
## 0:1-1:0 0.29988852 -0.2658039 0.8655809 0.5192046
## 1:1-1:0 0.24307692 -0.3104787 0.7966326 0.6682772
## 1:1-0:1 -0.05681159 -0.6277899 0.5141667 0.9940277# One-way ANOVA for SE_2 by bias
summary((anova_SE2 <- aov(SE_2 ~ as.factor(condition), data = wf_dt2)))## Df Sum Sq Mean Sq F value Pr(>F)
## as.factor(condition) 3 3.9 1.312 0.748 0.524
## Residuals 282 494.7 1.754
## 32 observations deleted due to missingness# Tukey post hoc test
(tukey_SE2 <- TukeyHSD(anova_SE2))## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = SE_2 ~ as.factor(condition), data = wf_dt2)
##
## $`as.factor(condition)`
## diff lwr upr p adj
## 1-0 0.07472826 -0.5192969 0.6687534 0.9881077
## 2-0 -0.22516026 -0.8024587 0.3521382 0.7448843
## 3-0 0.01791667 -0.5645624 0.6003957 0.9998195
## 2-1 -0.29988852 -0.8655809 0.2658039 0.5192046
## 3-1 -0.05681159 -0.6277899 0.5141667 0.9940277
## 3-2 0.24307692 -0.3104787 0.7966326 0.6682772# Independent samples t-test for BMIS_shame by condition
(t_test_BMIS_shame_2 <- t.test(SE_2 ~ bias, var.equal = TRUE, data = wf_dt2))##
## Two Sample t-test
##
## data: SE_2 by bias
## t = 0.92302, df = 284, p-value = 0.3568
## alternative hypothesis: true difference in means between group 0 and group 1 is not equal to 0
## 95 percent confidence interval:
## -0.1639581 0.4535055
## sample estimates:
## mean in group 0 mean in group 1
## 3.007519 2.862745# Calculating Cohen's d for BMIS_shame by condition
(cohen_d_BMIS_shame_2 <- cohensD(SE_2 ~ bias, data = wf_dt2))## [1] 0.1094265# Calculating standard deviations of BMIS_shame by condition
(sd_by_condition <- aggregate(SE_2 ~ bias, data = wf_dt2, FUN = sd))## bias SE_2
## 1 0 1.264289
## 2 1 1.371989wf_dt2$SE_2_Z <- scale(wf_dt2$SE_2, center = TRUE, scale = TRUE) #Z scores
wf_dt2$SE_1_Z <- scale(wf_dt2$SE1, center = TRUE, scale = TRUE) #Z scores
wf_dt2 <- wf_dt2 %>% mutate(SE_change = SE_2_Z - SE_1_Z) # higher scores indicate increases while negative score indicate decreases4. Model Regressions
# One-way ANOVA for purchase by interaction
summary((anova_purchase <- aov(purchase ~ as.factor(bias)*as.factor(brand_race), data = wf_dt2)))## Df Sum Sq Mean Sq F value Pr(>F)
## as.factor(bias) 1 32.9 32.91 8.053 0.00484 **
## as.factor(brand_race) 1 81.7 81.67 19.985 0.0000109 ***
## as.factor(bias):as.factor(brand_race) 1 18.4 18.44 4.511 0.03446 *
## Residuals 314 1283.2 4.09
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# Tukey post hoc test
(tukey_purchase <- TukeyHSD(anova_purchase))## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = purchase ~ as.factor(bias) * as.factor(brand_race), data = wf_dt2)
##
## $`as.factor(bias)`
## diff lwr upr p adj
## 1-0 0.6434335 0.1973273 1.08954 0.0048373
##
## $`as.factor(brand_race)`
## diff lwr upr p adj
## 1-0 1.014068 0.567538 1.460599 0.000011
##
## $`as.factor(bias):as.factor(brand_race)`
## diff lwr upr p adj
## 1:0-0:0 1.0729651 0.2619190 1.8840112 0.0039778
## 0:1-0:0 1.4956003 0.6676950 2.3235055 0.0000270
## 1:1-0:0 1.6040549 0.7876396 2.4204701 0.0000040
## 0:1-1:0 0.4226351 -0.4195067 1.2647769 0.5660145
## 1:1-1:0 0.5310897 -0.2997590 1.3619385 0.3516638
## 1:1-0:1 0.1084546 -0.7388593 0.9557685 0.98751595. Shame Regressions
summary(lm(BMIS_shame_2_Z ~ bias_aware_Z*as.factor(bias), wf_dt2)) #bias aware sig on its own and interaction##
## Call:
## lm(formula = BMIS_shame_2_Z ~ bias_aware_Z * as.factor(bias),
## data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.8994 -0.5751 -0.4242 0.5885 2.5843
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.28392 0.08072 -3.518 0.000508 ***
## bias_aware_Z 0.06689 0.08350 0.801 0.423730
## as.factor(bias)1 0.51996 0.11016 4.720 0.00000372 ***
## bias_aware_Z:as.factor(bias)1 0.29877 0.11051 2.704 0.007279 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9269 on 281 degrees of freedom
## (33 observations deleted due to missingness)
## Multiple R-squared: 0.1499, Adjusted R-squared: 0.1408
## F-statistic: 16.51 on 3 and 281 DF, p-value: 0.0000000006535BA interacts with bias condition to predit increased shame.
summary(lm(BMIS_shame_2_Z ~ defensive_reverse_Z*as.factor(bias), wf_dt2)) #defensiveness on its own not sig##
## Call:
## lm(formula = BMIS_shame_2_Z ~ defensive_reverse_Z * as.factor(bias),
## data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.4913 -0.7599 -0.3942 0.7591 2.5916
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.24669 0.08990 -2.744 0.00647 **
## defensive_reverse_Z 0.11327 0.09187 1.233 0.21865
## as.factor(bias)1 0.55590 0.12125 4.585 0.00000693 ***
## defensive_reverse_Z:as.factor(bias)1 -0.29353 0.12194 -2.407 0.01674 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9609 on 273 degrees of freedom
## (41 observations deleted due to missingness)
## Multiple R-squared: 0.09452, Adjusted R-squared: 0.08457
## F-statistic: 9.499 on 3 and 273 DF, p-value: 0.000005463summary(lm(BMIS_shame_2_Z ~ BMIS_shame_1_Z, wf_dt2)) # t1 and t2 are somewhat related##
## Call:
## lm(formula = BMIS_shame_2_Z ~ BMIS_shame_1_Z, data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.9437 -0.3044 -0.3044 0.1321 2.8093
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.003743 0.042316 -0.088 0.93
## BMIS_shame_1_Z 0.700904 0.042380 16.538 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7144 on 283 degrees of freedom
## (33 observations deleted due to missingness)
## Multiple R-squared: 0.4915, Adjusted R-squared: 0.4897
## F-statistic: 273.5 on 1 and 283 DF, p-value: < 0.00000000000000022summary(lm(BMIS_shame_2_Z ~ bias_discrepancy_Z*bias_aware_Z, wf_dt2)) # bias discrepancy is what predicts shame, it is sig when bias condition is in the model, but they do not interact (close at .24 with a postive ineraction). Does interact with bias aware such that those who were more bias aware reported a larger discrepancy and ultimatley reported more shame##
## Call:
## lm(formula = BMIS_shame_2_Z ~ bias_discrepancy_Z * bias_aware_Z,
## data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.6095 -0.6912 -0.3006 0.6511 2.6177
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.004705 0.055763 0.084 0.933
## bias_discrepancy_Z -0.257326 0.056514 -4.553 0.00000788 ***
## bias_aware_Z 0.267658 0.055618 4.812 0.00000244 ***
## bias_discrepancy_Z:bias_aware_Z -0.054683 0.060351 -0.906 0.366
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9358 on 281 degrees of freedom
## (33 observations deleted due to missingness)
## Multiple R-squared: 0.1335, Adjusted R-squared: 0.1242
## F-statistic: 14.43 on 3 and 281 DF, p-value: 0.000000009085summary(lm(shame_discrepancy ~ bias_discrepancy_Z, wf_dt2)) # larger bias discrepancy was associated with a greater change in shame, does not interact with as.factor bias##
## Call:
## lm(formula = shame_discrepancy ~ bias_discrepancy_Z, data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.37177 -0.29593 -0.03296 0.21531 2.83252
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.005542 0.043755 -0.127 0.899
## bias_discrepancy_Z -0.232661 0.043760 -5.317 0.000000215 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7387 on 283 degrees of freedom
## (33 observations deleted due to missingness)
## Multiple R-squared: 0.09082, Adjusted R-squared: 0.0876
## F-statistic: 28.27 on 1 and 283 DF, p-value: 0.0000002146summary(lm(shame_discrepancy ~ bias_aware_Z*as.factor(bias), wf_dt2)) # bias awareness by itself non sig, but interacts with bias condition to predict greater increases in shame ##
## Call:
## lm(formula = shame_discrepancy ~ bias_aware_Z * as.factor(bias),
## data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.33552 -0.38817 0.00329 0.25513 2.55957
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.29818 0.06271 -4.755 0.00000317436 ***
## bias_aware_Z -0.11342 0.06487 -1.748 0.0815 .
## as.factor(bias)1 0.53949 0.08558 6.304 0.00000000112 ***
## bias_aware_Z:as.factor(bias)1 0.22142 0.08586 2.579 0.0104 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7201 on 281 degrees of freedom
## (33 observations deleted due to missingness)
## Multiple R-squared: 0.1419, Adjusted R-squared: 0.1328
## F-statistic: 15.5 on 3 and 281 DF, p-value: 0.000000002351summary(lm(shame_discrepancy ~ defensive_reverse_Z, wf_dt2)) # defensiveness does not predict changes in shame ##
## Call:
## lm(formula = shame_discrepancy ~ defensive_reverse_Z, data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.43388 -0.23859 -0.10711 0.00122 3.10294
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.00155 0.04668 -0.033 0.974
## defensive_reverse_Z 0.07457 0.04675 1.595 0.112
##
## Residual standard error: 0.7768 on 275 degrees of freedom
## (41 observations deleted due to missingness)
## Multiple R-squared: 0.009166, Adjusted R-squared: 0.005563
## F-statistic: 2.544 on 1 and 275 DF, p-value: 0.1119summary(lm(shame_discrepancy ~ SE_change, wf_dt2)) # defensiveness does not predict changes in shame ##
## Call:
## lm(formula = shame_discrepancy ~ SE_change, data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.4376 -0.2412 -0.1151 0.1553 3.0150
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.003262 0.045435 -0.072 0.9428
## SE_change -0.357664 0.148738 -2.405 0.0168 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7669 on 283 degrees of freedom
## (33 observations deleted due to missingness)
## Multiple R-squared: 0.02002, Adjusted R-squared: 0.01656
## F-statistic: 5.782 on 1 and 283 DF, p-value: 0.016836. Defensiveness Regressions
summary(lm(defensive_reverse_Z ~ bias_aware_Z, wf_dt2)) ##
## Call:
## lm(formula = defensive_reverse_Z ~ bias_aware_Z, data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.34709 -0.70563 -0.05711 0.74082 1.97285
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.0001677 0.0594346 -0.003 0.9978
## bias_aware_Z -0.1451347 0.0589087 -2.464 0.0144 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.991 on 276 degrees of freedom
## (40 observations deleted due to missingness)
## Multiple R-squared: 0.02152, Adjusted R-squared: 0.01797
## F-statistic: 6.07 on 1 and 276 DF, p-value: 0.01436BA interacts with bias condition to predict decreased defensiveness.
6. Purchase Intentions
summary(lm(purchase_Z ~ as.factor(bias)*as.factor(brand_race) + defensive_reverse_Z, wf_dt2))##
## Call:
## lm(formula = purchase_Z ~ as.factor(bias) * as.factor(brand_race) +
## defensive_reverse_Z, data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.2422 -0.5772 0.1151 0.6103 1.6169
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.08270 0.10695 -0.773 0.44002
## as.factor(bias)1 0.18479 0.14389 1.284 0.20013
## as.factor(brand_race)1 0.35798 0.14791 2.420 0.01617 *
## defensive_reverse_Z -0.16558 0.05270 -3.142 0.00186 **
## as.factor(bias)1:as.factor(brand_race)1 -0.02875 0.20196 -0.142 0.88691
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8355 on 273 degrees of freedom
## (40 observations deleted due to missingness)
## Multiple R-squared: 0.07284, Adjusted R-squared: 0.05926
## F-statistic: 5.362 on 4 and 273 DF, p-value: 0.0003594summary(lm(purchase_Z ~ as.factor(bias)*defensive_reverse_Z, wf_dt2))##
## Call:
## lm(formula = purchase_Z ~ as.factor(bias) * defensive_reverse_Z,
## data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.26392 -0.53755 -0.01177 0.64252 1.48409
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.04760 0.07829 0.608 0.54364
## as.factor(bias)1 0.17488 0.10569 1.655 0.09914 .
## defensive_reverse_Z -0.33786 0.08001 -4.223 0.0000329 ***
## as.factor(bias)1:defensive_reverse_Z 0.31227 0.10629 2.938 0.00359 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8386 on 274 degrees of freedom
## (40 observations deleted due to missingness)
## Multiple R-squared: 0.06272, Adjusted R-squared: 0.05246
## F-statistic: 6.112 on 3 and 274 DF, p-value: 0.0004882summary(lm(purchase_Z ~ defensive_reverse_Z*as.factor(bias)*as.factor(brand_race), wf_dt2))##
## Call:
## lm(formula = purchase_Z ~ defensive_reverse_Z * as.factor(bias) *
## as.factor(brand_race), data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.37122 -0.45834 0.06874 0.59389 1.62104
##
## Coefficients:
## Estimate Std. Error
## (Intercept) -0.13578 0.10843
## defensive_reverse_Z -0.37578 0.11756
## as.factor(bias)1 0.23896 0.14365
## as.factor(brand_race)1 0.36614 0.15340
## defensive_reverse_Z:as.factor(bias)1 0.20370 0.14832
## defensive_reverse_Z:as.factor(brand_race)1 0.09006 0.15797
## as.factor(bias)1:as.factor(brand_race)1 -0.15253 0.20830
## defensive_reverse_Z:as.factor(bias)1:as.factor(brand_race)1 0.21994 0.21085
## t value Pr(>|t|)
## (Intercept) -1.252 0.21159
## defensive_reverse_Z -3.197 0.00156 **
## as.factor(bias)1 1.664 0.09737 .
## as.factor(brand_race)1 2.387 0.01768 *
## defensive_reverse_Z:as.factor(bias)1 1.373 0.17077
## defensive_reverse_Z:as.factor(brand_race)1 0.570 0.56909
## as.factor(bias)1:as.factor(brand_race)1 -0.732 0.46462
## defensive_reverse_Z:as.factor(bias)1:as.factor(brand_race)1 1.043 0.29783
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8212 on 270 degrees of freedom
## (40 observations deleted due to missingness)
## Multiple R-squared: 0.1143, Adjusted R-squared: 0.09132
## F-statistic: 4.977 on 7 and 270 DF, p-value: 0.00002575summary(lm(purchase_Z ~ defensive_reverse_Z + as.factor(brand_race), wf_dt2))##
## Call:
## lm(formula = purchase_Z ~ defensive_reverse_Z + as.factor(brand_race),
## data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.2881 -0.6357 0.1003 0.6190 1.6785
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.02069 0.07071 0.293 0.770041
## defensive_reverse_Z -0.14130 0.05027 -2.811 0.005298 **
## as.factor(brand_race)1 0.33556 0.10036 3.343 0.000942 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8365 on 275 degrees of freedom
## (40 observations deleted due to missingness)
## Multiple R-squared: 0.06382, Adjusted R-squared: 0.05701
## F-statistic: 9.373 on 2 and 275 DF, p-value: 0.0001153summary(lm(purchase_Z ~ defensive_reverse_Z*as.factor(brand_race)*as.factor(bias), wf_dt2))##
## Call:
## lm(formula = purchase_Z ~ defensive_reverse_Z * as.factor(brand_race) *
## as.factor(bias), data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.37122 -0.45834 0.06874 0.59389 1.62104
##
## Coefficients:
## Estimate Std. Error
## (Intercept) -0.13578 0.10843
## defensive_reverse_Z -0.37578 0.11756
## as.factor(brand_race)1 0.36614 0.15340
## as.factor(bias)1 0.23896 0.14365
## defensive_reverse_Z:as.factor(brand_race)1 0.09006 0.15797
## defensive_reverse_Z:as.factor(bias)1 0.20370 0.14832
## as.factor(brand_race)1:as.factor(bias)1 -0.15253 0.20830
## defensive_reverse_Z:as.factor(brand_race)1:as.factor(bias)1 0.21994 0.21085
## t value Pr(>|t|)
## (Intercept) -1.252 0.21159
## defensive_reverse_Z -3.197 0.00156 **
## as.factor(brand_race)1 2.387 0.01768 *
## as.factor(bias)1 1.664 0.09737 .
## defensive_reverse_Z:as.factor(brand_race)1 0.570 0.56909
## defensive_reverse_Z:as.factor(bias)1 1.373 0.17077
## as.factor(brand_race)1:as.factor(bias)1 -0.732 0.46462
## defensive_reverse_Z:as.factor(brand_race)1:as.factor(bias)1 1.043 0.29783
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8212 on 270 degrees of freedom
## (40 observations deleted due to missingness)
## Multiple R-squared: 0.1143, Adjusted R-squared: 0.09132
## F-statistic: 4.977 on 7 and 270 DF, p-value: 0.00002575summary(lm(rr_purchase ~ defensive_reverse_Z + as.factor(bias), wf_dt2))##
## Call:
## lm(formula = rr_purchase ~ defensive_reverse_Z + as.factor(bias),
## data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.9287 -0.5855 0.0003 1.4145 2.8524
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.3587 0.2191 19.896 <0.0000000000000002 ***
## defensive_reverse_Z -0.2016 0.1566 -1.288 0.200
## as.factor(bias)1 0.1479 0.3140 0.471 0.638
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.708 on 134 degrees of freedom
## (181 observations deleted due to missingness)
## Multiple R-squared: 0.01222, Adjusted R-squared: -0.002519
## F-statistic: 0.8291 on 2 and 134 DF, p-value: 0.4387summary(lm(rr_purchase ~ defensive_reverse_Z*as.factor(bias), wf_dt2))##
## Call:
## lm(formula = rr_purchase ~ defensive_reverse_Z * as.factor(bias),
## data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.3017 -0.7455 0.0462 1.3015 2.9132
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 4.1821 0.2184 19.150
## defensive_reverse_Z -0.6959 0.2140 -3.252
## as.factor(bias)1 0.1384 0.3033 0.456
## defensive_reverse_Z:as.factor(bias)1 0.9874 0.3025 3.265
## Pr(>|t|)
## (Intercept) < 0.0000000000000002 ***
## defensive_reverse_Z 0.00145 **
## as.factor(bias)1 0.64878
## defensive_reverse_Z:as.factor(bias)1 0.00139 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.649 on 133 degrees of freedom
## (181 observations deleted due to missingness)
## Multiple R-squared: 0.08551, Adjusted R-squared: 0.06488
## F-statistic: 4.145 on 3 and 133 DF, p-value: 0.007617summary(lm(rl_purchase ~ defensive_reverse_Z*as.factor(bias), wf_dt2))##
## Call:
## lm(formula = rl_purchase ~ defensive_reverse_Z * as.factor(bias),
## data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.4990 -1.4093 0.1638 1.1922 3.3408
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 3.4183 0.2289 14.936
## defensive_reverse_Z -0.8404 0.2476 -3.393
## as.factor(bias)1 0.4828 0.3030 1.593
## defensive_reverse_Z:as.factor(bias)1 0.5425 0.3140 1.728
## Pr(>|t|)
## (Intercept) < 0.0000000000000002 ***
## defensive_reverse_Z 0.000908 ***
## as.factor(bias)1 0.113408
## defensive_reverse_Z:as.factor(bias)1 0.086294 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.724 on 134 degrees of freedom
## (180 observations deleted due to missingness)
## Multiple R-squared: 0.09779, Adjusted R-squared: 0.07759
## F-statistic: 4.842 on 3 and 134 DF, p-value: 0.00313summary(lm(rr_purchase ~ defensive_reverse_Z + as.factor(bias), wf_dt2))##
## Call:
## lm(formula = rr_purchase ~ defensive_reverse_Z + as.factor(bias),
## data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.9287 -0.5855 0.0003 1.4145 2.8524
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.3587 0.2191 19.896 <0.0000000000000002 ***
## defensive_reverse_Z -0.2016 0.1566 -1.288 0.200
## as.factor(bias)1 0.1479 0.3140 0.471 0.638
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.708 on 134 degrees of freedom
## (181 observations deleted due to missingness)
## Multiple R-squared: 0.01222, Adjusted R-squared: -0.002519
## F-statistic: 0.8291 on 2 and 134 DF, p-value: 0.4387summary(lm(rl_purchase ~ defensive_reverse_Z + as.factor(bias), wf_dt2))##
## Call:
## lm(formula = rl_purchase ~ defensive_reverse_Z + as.factor(bias),
## data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.9028 -1.5245 0.2586 1.2195 3.4770
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.5005 0.2255 15.523 < 0.0000000000000002 ***
## defensive_reverse_Z -0.5028 0.1533 -3.279 0.00132 **
## as.factor(bias)1 0.4309 0.3037 1.419 0.15824
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.737 on 135 degrees of freedom
## (180 observations deleted due to missingness)
## Multiple R-squared: 0.07769, Adjusted R-squared: 0.06403
## F-statistic: 5.686 on 2 and 135 DF, p-value: 0.004258summary(lm(rr_purchase ~ defensive_reverse_Z, wf_dt2))##
## Call:
## lm(formula = rr_purchase ~ defensive_reverse_Z, data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.8012 -0.5475 -0.1248 1.3961 2.8752
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.4357 0.1455 30.479 <0.0000000000000002 ***
## defensive_reverse_Z -0.1746 0.1453 -1.202 0.231
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.703 on 135 degrees of freedom
## (181 observations deleted due to missingness)
## Multiple R-squared: 0.01059, Adjusted R-squared: 0.003259
## F-statistic: 1.445 on 1 and 135 DF, p-value: 0.2315summary(lm(rl_purchase ~ defensive_reverse_Z, wf_dt2))##
## Call:
## lm(formula = rl_purchase ~ defensive_reverse_Z, data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.6303 -1.5726 0.2232 1.3160 3.6315
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.7420 0.1485 25.206 < 0.0000000000000002 ***
## defensive_reverse_Z -0.4599 0.1509 -3.048 0.00277 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.743 on 136 degrees of freedom
## (180 observations deleted due to missingness)
## Multiple R-squared: 0.06394, Adjusted R-squared: 0.05705
## F-statistic: 9.289 on 1 and 136 DF, p-value: 0.00277summary(lm(rr_purchase ~ as.factor(bias), wf_dt2))##
## Call:
## lm(formula = rr_purchase ~ as.factor(bias), data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.5000 -0.5000 -0.4133 1.5000 2.5867
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.50000 0.20557 21.890 <0.0000000000000002 ***
## as.factor(bias)1 -0.08667 0.28386 -0.305 0.761
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.695 on 141 degrees of freedom
## (175 observations deleted due to missingness)
## Multiple R-squared: 0.0006607, Adjusted R-squared: -0.006427
## F-statistic: 0.09322 on 1 and 141 DF, p-value: 0.7606summary(lm(rl_purchase ~ as.factor(bias), wf_dt2))##
## Call:
## lm(formula = rl_purchase ~ as.factor(bias), data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.8571 -1.6032 0.1429 1.3968 3.3968
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.6032 0.2271 15.864 <0.0000000000000002 ***
## as.factor(bias)1 0.2540 0.3063 0.829 0.408
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.803 on 138 degrees of freedom
## (178 observations deleted due to missingness)
## Multiple R-squared: 0.004958, Adjusted R-squared: -0.002252
## F-statistic: 0.6877 on 1 and 138 DF, p-value: 0.4084# Create the scatter plot with a line for the interaction effect
ggplot(wf_dt2, aes(x = defensive_reverse_Z, y = rr_purchase, color = as.factor(bias))) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, linetype = "solid", aes(group = as.factor(bias))) +
labs(title = "Scatter Plot of Defensive Reverse and RR Shop Intentions by Bias",
x = "Defensive Reverse",
y = "RR Purchase Intentions",
color = "Bias") +
theme_minimal()## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 181 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 181 rows containing missing values (`geom_point()`).
The bias condition and the brand race condition interact to predict less support to purchase from black brands for people who are biased.This interaction does not hold when accounting for defensiveness. It seems to be the case that defensive responding is what is driving this effect where people who learn they are biased are slightly less support black brands than people who think they are not biased. It does seem like defensiveness in response to bias feedback does make participants more likely than others (no bias feedback) within the black brand condition to purchase from black brands. This is likely a form of morally affirming the self. Note that this pattern does not occur within the RL condition.
Ex predicting purchase_Z:as.factor(brand_race)1 0.35798 0.14791 2.420 0.01617 * defensive_reverse_Z -0.16558 0.05270 -3.142 0.00186 ** as.factor(bias)1:as.factor(brand_race)1 -0.02875 0.20196 -0.142 0.88691
Yet bias feedback does not impact willingness to purchase from a black business alone (i.e., RR_purchase), or in other words people learning that they were biased were not less likely to purchase from a black brand than others who did not learn that they were biased. Although they were more likely to purchase from a white business.
Ex predicting rr_purchase: defensive_reverse_Z -0.6959 0.2140 -3.252
0.00145 ** as.factor(bias)1 0.1384 0.3033 0.456 0.64878
defensive_reverse_Z:as.factor(bias)1 0.9874 0.3025 3.265 0.00139 **
summary(lm(rr_purchase ~ BMIS_shame_2_Z + as.factor(bias), wf_dt2))##
## Call:
## lm(formula = rr_purchase ~ BMIS_shame_2_Z + as.factor(bias),
## data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.5068 -0.5018 -0.4082 1.5031 2.5918
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.500710 0.207359 21.705 <0.0000000000000002 ***
## BMIS_shame_2_Z 0.004779 0.140676 0.034 0.973
## as.factor(bias)1 -0.088622 0.290628 -0.305 0.761
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.701 on 140 degrees of freedom
## (175 observations deleted due to missingness)
## Multiple R-squared: 0.0006689, Adjusted R-squared: -0.01361
## F-statistic: 0.04686 on 2 and 140 DF, p-value: 0.9542summary(lm(rr_purchase ~ BMIS_shame_2_Z*as.factor(bias), wf_dt2))##
## Call:
## lm(formula = rr_purchase ~ BMIS_shame_2_Z * as.factor(bias),
## data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.6824 -0.6234 -0.0380 1.3766 2.7669
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.4721 0.2088 21.420 <0.0000000000000002
## BMIS_shame_2_Z -0.1880 0.2233 -0.842 0.401
## as.factor(bias)1 -0.0930 0.2904 -0.320 0.749
## BMIS_shame_2_Z:as.factor(bias)1 0.3194 0.2874 1.111 0.268
##
## (Intercept) ***
## BMIS_shame_2_Z
## as.factor(bias)1
## BMIS_shame_2_Z:as.factor(bias)1
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.7 on 139 degrees of freedom
## (175 observations deleted due to missingness)
## Multiple R-squared: 0.009472, Adjusted R-squared: -0.01191
## F-statistic: 0.443 on 3 and 139 DF, p-value: 0.7226summary(lm(purchase_Z ~ BMIS_shame_2_Z*as.factor(brand_race) + as.factor(bias), wf_dt2))##
## Call:
## lm(formula = purchase_Z ~ BMIS_shame_2_Z * as.factor(brand_race) +
## as.factor(bias), data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.0411 -0.5982 0.1311 0.6980 1.7773
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.05009 0.09243 -0.542 0.588327
## BMIS_shame_2_Z -0.10448 0.07629 -1.370 0.171925
## as.factor(brand_race)1 0.36433 0.10030 3.632 0.000334 ***
## as.factor(bias)1 0.09846 0.10447 0.943 0.346746
## BMIS_shame_2_Z:as.factor(brand_race)1 0.10496 0.10100 1.039 0.299623
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8439 on 280 degrees of freedom
## (33 observations deleted due to missingness)
## Multiple R-squared: 0.05048, Adjusted R-squared: 0.03691
## F-statistic: 3.721 on 4 and 280 DF, p-value: 0.005721summary(lm(rl_purchase ~ BMIS_shame_2_Z*as.factor(bias), wf_dt2))##
## Call:
## lm(formula = rl_purchase ~ BMIS_shame_2_Z * as.factor(bias),
## data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.0819 -1.4163 0.1458 1.1458 3.5837
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.2487 0.2759 11.775 <0.0000000000000002
## BMIS_shame_2_Z -0.7524 0.3783 -1.989 0.0487
## as.factor(bias)1 0.6612 0.3452 1.915 0.0576
## BMIS_shame_2_Z:as.factor(bias)1 0.5386 0.4214 1.278 0.2034
##
## (Intercept) ***
## BMIS_shame_2_Z *
## as.factor(bias)1 .
## BMIS_shame_2_Z:as.factor(bias)1
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.776 on 135 degrees of freedom
## (179 observations deleted due to missingness)
## Multiple R-squared: 0.044, Adjusted R-squared: 0.02276
## F-statistic: 2.071 on 3 and 135 DF, p-value: 0.107summary(lm(rr_purchase ~ BMIS_shame_2_Z + as.factor(bias), wf_dt2))##
## Call:
## lm(formula = rr_purchase ~ BMIS_shame_2_Z + as.factor(bias),
## data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.5068 -0.5018 -0.4082 1.5031 2.5918
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.500710 0.207359 21.705 <0.0000000000000002 ***
## BMIS_shame_2_Z 0.004779 0.140676 0.034 0.973
## as.factor(bias)1 -0.088622 0.290628 -0.305 0.761
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.701 on 140 degrees of freedom
## (175 observations deleted due to missingness)
## Multiple R-squared: 0.0006689, Adjusted R-squared: -0.01361
## F-statistic: 0.04686 on 2 and 140 DF, p-value: 0.9542summary(lm(rl_purchase ~ BMIS_shame_2_Z + as.factor(bias), wf_dt2))##
## Call:
## lm(formula = rl_purchase ~ BMIS_shame_2_Z + as.factor(bias),
## data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.1918 -1.5310 0.3129 1.3129 3.7993
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.4309 0.2368 14.491 <0.0000000000000002 ***
## BMIS_shame_2_Z -0.3183 0.1671 -1.905 0.0588 .
## as.factor(bias)1 0.5047 0.3236 1.560 0.1211
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.781 on 136 degrees of freedom
## (179 observations deleted due to missingness)
## Multiple R-squared: 0.03243, Adjusted R-squared: 0.0182
## F-statistic: 2.279 on 2 and 136 DF, p-value: 0.1063summary(lm(rr_purchase ~ BMIS_shame_2_Z, wf_dt2))##
## Call:
## lm(formula = rr_purchase ~ BMIS_shame_2_Z, data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.4578 -0.4578 -0.4462 1.5422 2.5538
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.454791 0.142096 31.351 <0.0000000000000002 ***
## BMIS_shame_2_Z -0.003719 0.137444 -0.027 0.978
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.696 on 141 degrees of freedom
## (175 observations deleted due to missingness)
## Multiple R-squared: 5.193e-06, Adjusted R-squared: -0.007087
## F-statistic: 0.0007322 on 1 and 141 DF, p-value: 0.9785summary(lm(rl_purchase ~ BMIS_shame_2_Z, wf_dt2))##
## Call:
## lm(formula = rl_purchase ~ BMIS_shame_2_Z, data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.8991 -1.5431 0.1009 1.3383 3.8129
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.7150 0.1520 24.44 <0.0000000000000002 ***
## BMIS_shame_2_Z -0.2287 0.1577 -1.45 0.149
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.79 on 137 degrees of freedom
## (179 observations deleted due to missingness)
## Multiple R-squared: 0.01512, Adjusted R-squared: 0.007931
## F-statistic: 2.103 on 1 and 137 DF, p-value: 0.1493Shame does not seem to be related to purchase behavior at all.
summary(lm(rr_purchase ~ shame_discrepancy + as.factor(bias), wf_dt2))##
## Call:
## lm(formula = rr_purchase ~ shame_discrepancy + as.factor(bias),
## data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.8296 -0.5473 -0.0748 1.4527 2.7246
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.5697 0.2133 21.428 <0.0000000000000002 ***
## shame_discrepancy 0.2326 0.1932 1.204 0.231
## as.factor(bias)1 -0.2005 0.2988 -0.671 0.503
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.692 on 140 degrees of freedom
## (175 observations deleted due to missingness)
## Multiple R-squared: 0.0109, Adjusted R-squared: -0.00323
## F-statistic: 0.7714 on 2 and 140 DF, p-value: 0.4643summary(lm(rr_purchase ~ shame_discrepancy*as.factor(bias), wf_dt2))##
## Call:
## lm(formula = rr_purchase ~ shame_discrepancy * as.factor(bias),
## data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.0276 -0.5163 -0.0276 1.4847 2.7299
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 4.4953 0.2305 19.506
## shame_discrepancy -0.0158 0.3484 -0.045
## as.factor(bias)1 -0.1470 0.3055 -0.481
## shame_discrepancy:as.factor(bias)1 0.3590 0.4188 0.857
## Pr(>|t|)
## (Intercept) <0.0000000000000002 ***
## shame_discrepancy 0.964
## as.factor(bias)1 0.631
## shame_discrepancy:as.factor(bias)1 0.393
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.694 on 139 degrees of freedom
## (175 observations deleted due to missingness)
## Multiple R-squared: 0.0161, Adjusted R-squared: -0.005136
## F-statistic: 0.7582 on 3 and 139 DF, p-value: 0.5194summary(lm(purchase_Z ~ shame_discrepancy*as.factor(brand_race) + as.factor(bias), wf_dt2))##
## Call:
## lm(formula = purchase_Z ~ shame_discrepancy * as.factor(brand_race) +
## as.factor(bias), data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.0829 -0.7424 0.1644 0.6622 1.5985
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.001540 0.091944 0.017 0.986651
## shame_discrepancy 0.112011 0.095606 1.172 0.242361
## as.factor(brand_race)1 0.362735 0.100071 3.625 0.000343
## as.factor(bias)1 0.009943 0.106964 0.093 0.926002
## shame_discrepancy:as.factor(brand_race)1 -0.022127 0.129677 -0.171 0.864635
##
## (Intercept)
## shame_discrepancy
## as.factor(brand_race)1 ***
## as.factor(bias)1
## shame_discrepancy:as.factor(brand_race)1
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8435 on 280 degrees of freedom
## (33 observations deleted due to missingness)
## Multiple R-squared: 0.0514, Adjusted R-squared: 0.03785
## F-statistic: 3.793 on 4 and 280 DF, p-value: 0.00507summary(lm(rl_purchase ~ shame_discrepancy*as.factor(bias), wf_dt2))##
## Call:
## lm(formula = rl_purchase ~ shame_discrepancy * as.factor(bias),
## data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.9697 -1.5812 0.2311 1.4188 3.4188
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 3.58921 0.26089 13.758
## shame_discrepancy 0.08345 0.42078 0.198
## as.factor(bias)1 0.20098 0.34052 0.590
## shame_discrepancy:as.factor(bias)1 0.13819 0.48794 0.283
## Pr(>|t|)
## (Intercept) <0.0000000000000002 ***
## shame_discrepancy 0.843
## as.factor(bias)1 0.556
## shame_discrepancy:as.factor(bias)1 0.777
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.805 on 135 degrees of freedom
## (179 observations deleted due to missingness)
## Multiple R-squared: 0.01277, Adjusted R-squared: -0.009165
## F-statistic: 0.5823 on 3 and 135 DF, p-value: 0.6276summary(lm(rr_purchase ~ shame_discrepancy + as.factor(bias), wf_dt2))##
## Call:
## lm(formula = rr_purchase ~ shame_discrepancy + as.factor(bias),
## data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.8296 -0.5473 -0.0748 1.4527 2.7246
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.5697 0.2133 21.428 <0.0000000000000002 ***
## shame_discrepancy 0.2326 0.1932 1.204 0.231
## as.factor(bias)1 -0.2005 0.2988 -0.671 0.503
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.692 on 140 degrees of freedom
## (175 observations deleted due to missingness)
## Multiple R-squared: 0.0109, Adjusted R-squared: -0.00323
## F-statistic: 0.7714 on 2 and 140 DF, p-value: 0.4643summary(lm(rl_purchase ~ shame_discrepancy + as.factor(bias), wf_dt2))##
## Call:
## lm(formula = rl_purchase ~ shame_discrepancy + as.factor(bias),
## data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.952 -1.602 0.217 1.398 3.398
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.6196 0.2370 15.275 <0.0000000000000002 ***
## shame_discrepancy 0.1862 0.2123 0.877 0.382
## as.factor(bias)1 0.1813 0.3322 0.546 0.586
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.799 on 136 degrees of freedom
## (179 observations deleted due to missingness)
## Multiple R-squared: 0.01219, Adjusted R-squared: -0.00234
## F-statistic: 0.8389 on 2 and 136 DF, p-value: 0.4344summary(lm(rr_purchase ~ shame_discrepancy, wf_dt2))##
## Call:
## lm(formula = rr_purchase ~ shame_discrepancy, data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.8420 -0.4444 -0.1952 1.5556 2.7796
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.4628 0.1415 31.544 <0.0000000000000002 ***
## shame_discrepancy 0.1915 0.1829 1.047 0.297
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.689 on 141 degrees of freedom
## (175 observations deleted due to missingness)
## Multiple R-squared: 0.007718, Adjusted R-squared: 0.0006803
## F-statistic: 1.097 on 1 and 141 DF, p-value: 0.2968summary(lm(rl_purchase ~ shame_discrepancy, wf_dt2))##
## Call:
## lm(formula = rl_purchase ~ shame_discrepancy, data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.9052 -1.6811 0.3037 1.3037 3.3340
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.7185 0.1524 24.406 <0.0000000000000002 ***
## shame_discrepancy 0.2305 0.1957 1.178 0.241
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.794 on 137 degrees of freedom
## (179 observations deleted due to missingness)
## Multiple R-squared: 0.01002, Adjusted R-squared: 0.002798
## F-statistic: 1.387 on 1 and 137 DF, p-value: 0.2409summary(lm(rr_purchase ~ bias_aware_Z + as.factor(bias), wf_dt2))##
## Call:
## lm(formula = rr_purchase ~ bias_aware_Z + as.factor(bias), data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.6200 -0.5586 -0.2833 1.5194 2.6877
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.48586 0.20759 21.609 <0.0000000000000002 ***
## bias_aware_Z -0.08485 0.15050 -0.564 0.574
## as.factor(bias)1 -0.07584 0.28519 -0.266 0.791
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.699 on 140 degrees of freedom
## (175 observations deleted due to missingness)
## Multiple R-squared: 0.002924, Adjusted R-squared: -0.01132
## F-statistic: 0.2053 on 2 and 140 DF, p-value: 0.8147summary(lm(rr_purchase ~ bias_aware_Z*as.factor(bias), wf_dt2))##
## Call:
## lm(formula = rr_purchase ~ bias_aware_Z * as.factor(bias), data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.6510 -0.5174 -0.2006 1.4970 2.7519
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.49767 0.21020 21.397 <0.0000000000000002
## bias_aware_Z -0.01396 0.22967 -0.061 0.952
## as.factor(bias)1 -0.08975 0.28806 -0.312 0.756
## bias_aware_Z:as.factor(bias)1 -0.12481 0.30474 -0.410 0.683
##
## (Intercept) ***
## bias_aware_Z
## as.factor(bias)1
## bias_aware_Z:as.factor(bias)1
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.704 on 139 degrees of freedom
## (175 observations deleted due to missingness)
## Multiple R-squared: 0.004126, Adjusted R-squared: -0.01737
## F-statistic: 0.192 on 3 and 139 DF, p-value: 0.9017summary(lm(purchase_Z ~ bias_aware_Z*as.factor(brand_race)*as.factor(bias), wf_dt2))##
## Call:
## lm(formula = purchase_Z ~ bias_aware_Z * as.factor(brand_race) *
## as.factor(bias), data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.0752 -0.8039 0.1340 0.7867 1.7780
##
## Coefficients:
## Estimate Std. Error
## (Intercept) -0.192968 0.111792
## bias_aware_Z -0.001280 0.109804
## as.factor(brand_race)1 0.408333 0.156454
## as.factor(bias)1 0.222477 0.153118
## bias_aware_Z:as.factor(brand_race)1 -0.108982 0.163828
## bias_aware_Z:as.factor(bias)1 -0.002967 0.146318
## as.factor(brand_race)1:as.factor(bias)1 -0.159722 0.215868
## bias_aware_Z:as.factor(brand_race)1:as.factor(bias)1 0.063702 0.218724
## t value Pr(>|t|)
## (Intercept) -1.726 0.08537 .
## bias_aware_Z -0.012 0.99070
## as.factor(brand_race)1 2.610 0.00952 **
## as.factor(bias)1 1.453 0.14730
## bias_aware_Z:as.factor(brand_race)1 -0.665 0.50643
## bias_aware_Z:as.factor(bias)1 -0.020 0.98384
## as.factor(brand_race)1:as.factor(bias)1 -0.740 0.45995
## bias_aware_Z:as.factor(brand_race)1:as.factor(bias)1 0.291 0.77107
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9325 on 294 degrees of freedom
## (16 observations deleted due to missingness)
## Multiple R-squared: 0.04148, Adjusted R-squared: 0.01866
## F-statistic: 1.817 on 7 and 294 DF, p-value: 0.08359summary(lm(rl_purchase ~ bias_aware_Z*as.factor(bias), wf_dt2))##
## Call:
## lm(formula = rl_purchase ~ bias_aware_Z * as.factor(bias), data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.0493 -1.6807 0.1642 1.2466 3.4411
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.61735 0.22971 15.748 <0.0000000000000002
## bias_aware_Z -0.12471 0.22353 -0.558 0.578
## as.factor(bias)1 0.25195 0.30974 0.813 0.417
## bias_aware_Z:as.factor(bias)1 0.02197 0.29586 0.074 0.941
##
## (Intercept) ***
## bias_aware_Z
## as.factor(bias)1
## bias_aware_Z:as.factor(bias)1
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.812 on 136 degrees of freedom
## (178 observations deleted due to missingness)
## Multiple R-squared: 0.009273, Adjusted R-squared: -0.01258
## F-statistic: 0.4243 on 3 and 136 DF, p-value: 0.7359summary(lm(rr_purchase ~ bias_aware_Z + as.factor(bias), wf_dt2))##
## Call:
## lm(formula = rr_purchase ~ bias_aware_Z + as.factor(bias), data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.6200 -0.5586 -0.2833 1.5194 2.6877
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.48586 0.20759 21.609 <0.0000000000000002 ***
## bias_aware_Z -0.08485 0.15050 -0.564 0.574
## as.factor(bias)1 -0.07584 0.28519 -0.266 0.791
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.699 on 140 degrees of freedom
## (175 observations deleted due to missingness)
## Multiple R-squared: 0.002924, Adjusted R-squared: -0.01132
## F-statistic: 0.2053 on 2 and 140 DF, p-value: 0.8147summary(lm(rl_purchase ~ bias_aware_Z + as.factor(bias), wf_dt2))##
## Call:
## lm(formula = rl_purchase ~ bias_aware_Z + as.factor(bias), data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.0669 -1.6646 0.1876 1.2554 3.4367
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.6159 0.2281 15.854 <0.0000000000000002 ***
## bias_aware_Z -0.1122 0.1459 -0.769 0.443
## as.factor(bias)1 0.2545 0.3067 0.830 0.408
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.805 on 137 degrees of freedom
## (178 observations deleted due to missingness)
## Multiple R-squared: 0.009233, Adjusted R-squared: -0.005231
## F-statistic: 0.6383 on 2 and 137 DF, p-value: 0.5297summary(lm(rr_purchase ~ bias_aware_Z, wf_dt2))##
## Call:
## lm(formula = rr_purchase ~ bias_aware_Z, data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.5992 -0.5393 -0.3001 1.5055 2.6550
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.44582 0.14242 31.217 <0.0000000000000002 ***
## bias_aware_Z -0.08754 0.14967 -0.585 0.56
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.694 on 141 degrees of freedom
## (175 observations deleted due to missingness)
## Multiple R-squared: 0.002421, Adjusted R-squared: -0.004654
## F-statistic: 0.3421 on 1 and 141 DF, p-value: 0.5595summary(lm(rl_purchase ~ bias_aware_Z, wf_dt2))##
## Call:
## lm(formula = rl_purchase ~ bias_aware_Z, data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.9519 -1.6126 0.2106 1.2775 3.5068
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.7559 0.1534 24.491 <0.0000000000000002 ***
## bias_aware_Z -0.1119 0.1457 -0.768 0.444
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.803 on 138 degrees of freedom
## (178 observations deleted due to missingness)
## Multiple R-squared: 0.004254, Adjusted R-squared: -0.002962
## F-statistic: 0.5895 on 1 and 138 DF, p-value: 0.4439Bias awareness does not predict anything.
6. Shop Intentions
summary(lm(shop_intentions_Z ~ as.factor(bias)*as.factor(brand_race), wf_dt2)) # main interaction working, it is defensiveness driving this effect, whe defensiveness is included as a term in this model the effect vanishes, but defensiveness on its own is still negatively correlated with the dv, it also loses its sig for shame and se, but they are not signifgicant predictors of the dv##
## Call:
## lm(formula = shop_intentions_Z ~ as.factor(bias) * as.factor(brand_race),
## data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.03843 -0.86980 0.06421 0.76362 1.92776
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.4460 0.1038 -4.297 0.00002310
## as.factor(bias)1 0.4625 0.1495 3.094 0.00215
## as.factor(brand_race)1 0.6971 0.1526 4.568 0.00000707
## as.factor(bias)1:as.factor(brand_race)1 -0.4773 0.2162 -2.208 0.02800
##
## (Intercept) ***
## as.factor(bias)1 **
## as.factor(brand_race)1 ***
## as.factor(bias)1:as.factor(brand_race)1 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9624 on 314 degrees of freedom
## Multiple R-squared: 0.08251, Adjusted R-squared: 0.07374
## F-statistic: 9.412 on 3 and 314 DF, p-value: 0.000005659summary(lm(shop_intentions ~ shame_discrepancy , wf_dt2)) # increases in shame from t1 to t2 are associated with increases in shopping attentions, this effect is for across all of the conditions ##
## Call:
## lm(formula = shop_intentions ~ shame_discrepancy, data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.2249 -1.2249 -0.1547 1.2210 3.3995
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.2505 0.1066 39.858 <0.0000000000000002 ***
## shame_discrepancy 0.2670 0.1381 1.932 0.0543 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.8 on 283 degrees of freedom
## (33 observations deleted due to missingness)
## Multiple R-squared: 0.01302, Adjusted R-squared: 0.009537
## F-statistic: 3.734 on 1 and 283 DF, p-value: 0.0543summary(lm(shop_intentions_Z ~ as.factor(bias)*as.factor(brand_race) + defensive_reverse_Z, wf_dt2))##
## Call:
## lm(formula = shop_intentions_Z ~ as.factor(bias) * as.factor(brand_race) +
## defensive_reverse_Z, data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.0582 -0.4291 0.1117 0.5622 1.5260
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.02147 0.10528 -0.204 0.83860
## as.factor(bias)1 0.11232 0.14165 0.793 0.42850
## as.factor(brand_race)1 0.34435 0.14562 2.365 0.01874 *
## defensive_reverse_Z -0.16625 0.05188 -3.204 0.00151 **
## as.factor(bias)1:as.factor(brand_race)1 -0.04522 0.19882 -0.227 0.82023
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8226 on 273 degrees of freedom
## (40 observations deleted due to missingness)
## Multiple R-squared: 0.06969, Adjusted R-squared: 0.05606
## F-statistic: 5.113 on 4 and 273 DF, p-value: 0.0005487summary(lm(shop_intentions_Z ~ defensive_reverse_Z*as.factor(bias), wf_dt2))##
## Call:
## lm(formula = shop_intentions_Z ~ defensive_reverse_Z * as.factor(bias),
## data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.97049 -0.48680 0.00777 0.55890 1.65826
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.10089 0.07684 1.313 0.19032
## defensive_reverse_Z -0.34146 0.07854 -4.348 0.0000194 ***
## as.factor(bias)1 0.09565 0.10374 0.922 0.35734
## defensive_reverse_Z:as.factor(bias)1 0.31618 0.10433 3.031 0.00267 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8231 on 274 degrees of freedom
## (40 observations deleted due to missingness)
## Multiple R-squared: 0.06508, Adjusted R-squared: 0.05485
## F-statistic: 6.358 on 3 and 274 DF, p-value: 0.0003513summary(lm(shop_intentions_Z ~ defensive_reverse_Z*as.factor(bias)*as.factor(brand_race), wf_dt2))##
## Call:
## lm(formula = shop_intentions_Z ~ defensive_reverse_Z * as.factor(bias) *
## as.factor(brand_race), data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.1018 -0.4540 0.1415 0.5392 1.5492
##
## Coefficients:
## Estimate Std. Error
## (Intercept) -0.06561 0.10702
## defensive_reverse_Z -0.34106 0.11603
## as.factor(bias)1 0.15278 0.14178
## as.factor(brand_race)1 0.33061 0.15140
## defensive_reverse_Z:as.factor(bias)1 0.19666 0.14639
## defensive_reverse_Z:as.factor(brand_race)1 0.01997 0.15592
## as.factor(bias)1:as.factor(brand_race)1 -0.12989 0.20559
## defensive_reverse_Z:as.factor(bias)1:as.factor(brand_race)1 0.22875 0.20811
## t value Pr(>|t|)
## (Intercept) -0.613 0.54038
## defensive_reverse_Z -2.939 0.00357 **
## as.factor(bias)1 1.078 0.28220
## as.factor(brand_race)1 2.184 0.02985 *
## defensive_reverse_Z:as.factor(bias)1 1.343 0.18029
## defensive_reverse_Z:as.factor(brand_race)1 0.128 0.89818
## as.factor(bias)1:as.factor(brand_race)1 -0.632 0.52805
## defensive_reverse_Z:as.factor(bias)1:as.factor(brand_race)1 1.099 0.27268
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8105 on 270 degrees of freedom
## (40 observations deleted due to missingness)
## Multiple R-squared: 0.1067, Adjusted R-squared: 0.0835
## F-statistic: 4.605 on 7 and 270 DF, p-value: 0.00006965summary(lm(shop_intentions_Z ~ defensive_reverse_Z + as.factor(brand_race), wf_dt2))##
## Call:
## lm(formula = shop_intentions_Z ~ defensive_reverse_Z + as.factor(brand_race),
## data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.00042 -0.45018 0.09071 0.57601 1.56561
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.04133 0.06937 0.596 0.55182
## defensive_reverse_Z -0.15408 0.04932 -3.124 0.00198 **
## as.factor(brand_race)1 0.31634 0.09847 3.212 0.00147 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8208 on 275 degrees of freedom
## (40 observations deleted due to missingness)
## Multiple R-squared: 0.06692, Adjusted R-squared: 0.06014
## F-statistic: 9.862 on 2 and 275 DF, p-value: 0.00007307summary(lm(shop_intentions_Z ~ defensive_reverse_Z*as.factor(brand_race), wf_dt2))##
## Call:
## lm(formula = shop_intentions_Z ~ defensive_reverse_Z * as.factor(brand_race),
## data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.8980 -0.3948 0.1070 0.6027 1.6065
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.04045 0.06938 0.583 0.56035
## defensive_reverse_Z -0.20333 0.07003 -2.903 0.00399
## as.factor(brand_race)1 0.31634 0.09848 3.212 0.00147
## defensive_reverse_Z:as.factor(brand_race)1 0.09775 0.09865 0.991 0.32265
##
## (Intercept)
## defensive_reverse_Z **
## as.factor(brand_race)1 **
## defensive_reverse_Z:as.factor(brand_race)1
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8208 on 274 degrees of freedom
## (40 observations deleted due to missingness)
## Multiple R-squared: 0.07025, Adjusted R-squared: 0.06007
## F-statistic: 6.901 on 3 and 274 DF, p-value: 0.0001701summary(lm(rr_shop_intentions ~ defensive_reverse_Z + as.factor(bias), wf_dt2))##
## Call:
## lm(formula = rr_shop_intentions ~ defensive_reverse_Z + as.factor(bias),
## data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.1108 -0.6707 0.2284 1.3463 2.8034
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.56328 0.21883 20.85 <0.0000000000000002 ***
## defensive_reverse_Z -0.23602 0.15633 -1.51 0.133
## as.factor(bias)1 0.05356 0.31468 0.17 0.865
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.713 on 135 degrees of freedom
## (180 observations deleted due to missingness)
## Multiple R-squared: 0.01789, Adjusted R-squared: 0.00334
## F-statistic: 1.23 on 2 and 135 DF, p-value: 0.2957summary(lm(rr_shop_intentions ~ defensive_reverse_Z*as.factor(bias), wf_dt2))##
## Call:
## lm(formula = rr_shop_intentions ~ defensive_reverse_Z * as.factor(bias),
## data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.5006 -0.7331 0.2762 1.2148 3.1640
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 4.39448 0.21995 19.979
## defensive_reverse_Z -0.68755 0.21389 -3.214
## as.factor(bias)1 0.04901 0.30577 0.160
## defensive_reverse_Z:as.factor(bias)1 0.91092 0.30380 2.998
## Pr(>|t|)
## (Intercept) < 0.0000000000000002 ***
## defensive_reverse_Z 0.00164 **
## as.factor(bias)1 0.87291
## defensive_reverse_Z:as.factor(bias)1 0.00324 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.665 on 134 degrees of freedom
## (180 observations deleted due to missingness)
## Multiple R-squared: 0.07964, Adjusted R-squared: 0.05903
## F-statistic: 3.865 on 3 and 134 DF, p-value: 0.01088summary(lm(rl_shop_intentions ~ defensive_reverse_Z*as.factor(bias), wf_dt2))##
## Call:
## lm(formula = rl_shop_intentions ~ defensive_reverse_Z * as.factor(bias),
## data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.6258 -1.2417 0.2224 1.0750 3.3173
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 3.6866 0.2355 15.655
## defensive_reverse_Z -0.7303 0.2553 -2.860
## as.factor(bias)1 0.3616 0.3125 1.157
## defensive_reverse_Z:as.factor(bias)1 0.4919 0.3241 1.518
## Pr(>|t|)
## (Intercept) <0.0000000000000002 ***
## defensive_reverse_Z 0.0049 **
## as.factor(bias)1 0.2492
## defensive_reverse_Z:as.factor(bias)1 0.1314
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.783 on 135 degrees of freedom
## (179 observations deleted due to missingness)
## Multiple R-squared: 0.06784, Adjusted R-squared: 0.04713
## F-statistic: 3.275 on 3 and 135 DF, p-value: 0.02313summary(lm(rr_shop_intentions ~ defensive_reverse_Z + as.factor(bias), wf_dt2))##
## Call:
## lm(formula = rr_shop_intentions ~ defensive_reverse_Z + as.factor(bias),
## data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.1108 -0.6707 0.2284 1.3463 2.8034
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.56328 0.21883 20.85 <0.0000000000000002 ***
## defensive_reverse_Z -0.23602 0.15633 -1.51 0.133
## as.factor(bias)1 0.05356 0.31468 0.17 0.865
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.713 on 135 degrees of freedom
## (180 observations deleted due to missingness)
## Multiple R-squared: 0.01789, Adjusted R-squared: 0.00334
## F-statistic: 1.23 on 2 and 135 DF, p-value: 0.2957summary(lm(rl_shop_intentions ~ defensive_reverse_Z + as.factor(bias), wf_dt2))##
## Call:
## lm(formula = rl_shop_intentions ~ defensive_reverse_Z + as.factor(bias),
## data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.6909 -1.3344 0.3758 1.0825 3.2694
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.7637 0.2311 16.29 < 0.0000000000000002 ***
## defensive_reverse_Z -0.4250 0.1580 -2.69 0.00805 **
## as.factor(bias)1 0.3121 0.3122 1.00 0.31926
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.792 on 136 degrees of freedom
## (179 observations deleted due to missingness)
## Multiple R-squared: 0.05194, Adjusted R-squared: 0.03799
## F-statistic: 3.725 on 2 and 136 DF, p-value: 0.02661summary(lm(rr_shop_intentions ~ defensive_reverse_Z, wf_dt2))##
## Call:
## lm(formula = rr_shop_intentions ~ defensive_reverse_Z, data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.0643 -0.6902 0.2277 1.3736 2.8115
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.5910 0.1453 31.588 <0.0000000000000002 ***
## defensive_reverse_Z -0.2261 0.1445 -1.564 0.12
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.707 on 136 degrees of freedom
## (180 observations deleted due to missingness)
## Multiple R-squared: 0.01768, Adjusted R-squared: 0.01046
## F-statistic: 2.448 on 1 and 136 DF, p-value: 0.12summary(lm(rl_shop_intentions ~ defensive_reverse_Z, wf_dt2))##
## Call:
## lm(formula = rl_shop_intentions ~ defensive_reverse_Z, data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.5064 -1.3403 0.3817 1.1597 3.3817
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.9375 0.1521 25.89 <0.0000000000000002 ***
## defensive_reverse_Z -0.3930 0.1547 -2.54 0.0122 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.792 on 137 degrees of freedom
## (179 observations deleted due to missingness)
## Multiple R-squared: 0.04497, Adjusted R-squared: 0.038
## F-statistic: 6.451 on 1 and 137 DF, p-value: 0.01221summary(lm(rr_shop_intentions ~ as.factor(bias), wf_dt2))##
## Call:
## lm(formula = rr_shop_intentions ~ as.factor(bias), data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.6812 -0.6812 0.3188 1.3188 2.4933
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.6812 0.2045 22.891 <0.0000000000000002 ***
## as.factor(bias)1 -0.1745 0.2834 -0.616 0.539
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.699 on 142 degrees of freedom
## (174 observations deleted due to missingness)
## Multiple R-squared: 0.002663, Adjusted R-squared: -0.00436
## F-statistic: 0.3792 on 1 and 142 DF, p-value: 0.539summary(lm(rl_shop_intentions ~ as.factor(bias), wf_dt2))##
## Call:
## lm(formula = rl_shop_intentions ~ as.factor(bias), data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.0130 -1.8594 0.1406 1.1406 3.1406
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.8594 0.2305 16.742 <0.0000000000000002 ***
## as.factor(bias)1 0.1536 0.3119 0.492 0.623
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.844 on 139 degrees of freedom
## (177 observations deleted due to missingness)
## Multiple R-squared: 0.001742, Adjusted R-squared: -0.00544
## F-statistic: 0.2425 on 1 and 139 DF, p-value: 0.6232# Create an interaction plot
interaction.plot(
x.factor = wf_dt2$brand_race,
trace.factor = wf_dt2$bias,
response = wf_dt2$shop_intentions_Z,
type = "b", col = c("red", "blue"), pch = c(1, 2),
main = "Interaction Plot of Bias and Brand Race on Shop Intentions",
xlab = "Brand race",
ylab = "Shop Intentions",
legend = TRUE
)
# Create the scatter plot with a line for the interaction effect
ggplot(wf_dt2, aes(x = defensive_reverse_Z, y = shop_intentions_Z, color = as.factor(bias))) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, linetype = "solid", aes(group = as.factor(bias))) +
labs(title = "Scatter Plot of Defensive Reverse and RR Shop Intentions by Bias",
x = "Defensive Reverse",
y = "RR Shop Intentions",
color = "Bias") +
theme_minimal()## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 40 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 40 rows containing missing values (`geom_point()`).
Similar to purchase intentions, the main effect is being driven by defensiveness, those who learned that they are biased are less likely to purchase from a black brand than non-biased people, but when you account for this effect it vanishes. What you see when you examine the relationship between defensiveness and bias feedback is that those who are in the bias feedback condition are do not change in their shopping intentions the more defensive they are, while those in the no bias condition become more defensive.
summary(lm(rr_shop_intentions ~ BMIS_shame_2_Z + as.factor(bias), wf_dt2))##
## Call:
## lm(formula = rr_shop_intentions ~ BMIS_shame_2_Z + as.factor(bias),
## data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.6871 -0.6776 0.3129 1.3153 2.5122
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.679702 0.206417 22.671 <0.0000000000000002 ***
## BMIS_shame_2_Z -0.009222 0.140755 -0.066 0.948
## as.factor(bias)1 -0.170631 0.290400 -0.588 0.558
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.705 on 141 degrees of freedom
## (174 observations deleted due to missingness)
## Multiple R-squared: 0.002694, Adjusted R-squared: -0.01145
## F-statistic: 0.1904 on 2 and 141 DF, p-value: 0.8268summary(lm(rr_shop_intentions ~ BMIS_shame_2_Z*as.factor(bias) + defensive_reverse_Z, wf_dt2))##
## Call:
## lm(formula = rr_shop_intentions ~ BMIS_shame_2_Z * as.factor(bias) +
## defensive_reverse_Z, data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.0405 -0.7375 0.1724 1.2527 2.7653
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.5413 0.2213 20.523 <0.0000000000000002
## BMIS_shame_2_Z -0.2145 0.2328 -0.922 0.358
## as.factor(bias)1 0.0216 0.3229 0.067 0.947
## defensive_reverse_Z -0.2030 0.1591 -1.276 0.204
## BMIS_shame_2_Z:as.factor(bias)1 0.3573 0.3003 1.190 0.236
##
## (Intercept) ***
## BMIS_shame_2_Z
## as.factor(bias)1
## defensive_reverse_Z
## BMIS_shame_2_Z:as.factor(bias)1
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.717 on 133 degrees of freedom
## (180 observations deleted due to missingness)
## Multiple R-squared: 0.02824, Adjusted R-squared: -0.0009891
## F-statistic: 0.9662 on 4 and 133 DF, p-value: 0.4284summary(lm(shop_intentions_Z ~ BMIS_shame_2_Z*as.factor(brand_race) + as.factor(bias), wf_dt2))##
## Call:
## lm(formula = shop_intentions_Z ~ BMIS_shame_2_Z * as.factor(brand_race) +
## as.factor(bias), data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.90162 -0.38019 0.08682 0.63467 1.65863
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.01575 0.09122 0.173 0.863048
## BMIS_shame_2_Z -0.09352 0.07529 -1.242 0.215190
## as.factor(brand_race)1 0.32940 0.09898 3.328 0.000992 ***
## as.factor(bias)1 0.02334 0.10310 0.226 0.821031
## BMIS_shame_2_Z:as.factor(brand_race)1 0.07908 0.09967 0.793 0.428235
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8328 on 280 degrees of freedom
## (33 observations deleted due to missingness)
## Multiple R-squared: 0.04186, Adjusted R-squared: 0.02818
## F-statistic: 3.059 on 4 and 280 DF, p-value: 0.01722summary(lm(shop_intentions_Z ~ BMIS_shame_2_Z*as.factor(brand_race), wf_dt2))##
## Call:
## lm(formula = shop_intentions_Z ~ BMIS_shame_2_Z * as.factor(brand_race),
## data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.88813 -0.39432 0.07269 0.64785 1.65929
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.02892 0.07016 0.412 0.680542
## BMIS_shame_2_Z -0.08940 0.07292 -1.226 0.221270
## as.factor(brand_race)1 0.32825 0.09868 3.326 0.000997 ***
## BMIS_shame_2_Z:as.factor(brand_race)1 0.07725 0.09918 0.779 0.436695
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8314 on 281 degrees of freedom
## (33 observations deleted due to missingness)
## Multiple R-squared: 0.04169, Adjusted R-squared: 0.03146
## F-statistic: 4.075 on 3 and 281 DF, p-value: 0.007428summary(lm(shop_intentions_Z ~ BMIS_shame_2_Z*as.factor(bias) + defensive_reverse_Z, wf_dt2))##
## Call:
## lm(formula = shop_intentions_Z ~ BMIS_shame_2_Z * as.factor(bias) +
## defensive_reverse_Z, data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.8724 -0.4427 0.1191 0.5428 1.5247
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.11380 0.07993 1.424 0.15564
## BMIS_shame_2_Z -0.14464 0.09406 -1.538 0.12528
## as.factor(bias)1 0.11728 0.10967 1.069 0.28585
## defensive_reverse_Z -0.15344 0.05310 -2.890 0.00417 **
## BMIS_shame_2_Z:as.factor(bias)1 0.14429 0.11374 1.269 0.20566
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8351 on 272 degrees of freedom
## (41 observations deleted due to missingness)
## Multiple R-squared: 0.04132, Adjusted R-squared: 0.02723
## F-statistic: 2.931 on 4 and 272 DF, p-value: 0.02129summary(lm(rl_shop_intentions ~ BMIS_shame_2_Z*as.factor(bias), wf_dt2))##
## Call:
## lm(formula = rl_shop_intentions ~ BMIS_shame_2_Z * as.factor(bias),
## data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.1795 -1.2096 -0.1443 1.1149 3.2445
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.4274 0.2812 12.188 <0.0000000000000002
## BMIS_shame_2_Z -0.9345 0.3849 -2.428 0.0165
## as.factor(bias)1 0.6164 0.3521 1.751 0.0823
## BMIS_shame_2_Z:as.factor(bias)1 0.8097 0.4291 1.887 0.0613
##
## (Intercept) ***
## BMIS_shame_2_Z *
## as.factor(bias)1 .
## BMIS_shame_2_Z:as.factor(bias)1 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.813 on 136 degrees of freedom
## (178 observations deleted due to missingness)
## Multiple R-squared: 0.04694, Adjusted R-squared: 0.02592
## F-statistic: 2.233 on 3 and 136 DF, p-value: 0.08721summary(lm(rr_shop_intentions ~ BMIS_shame_2_Z + as.factor(bias), wf_dt2))##
## Call:
## lm(formula = rr_shop_intentions ~ BMIS_shame_2_Z + as.factor(bias),
## data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.6871 -0.6776 0.3129 1.3153 2.5122
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.679702 0.206417 22.671 <0.0000000000000002 ***
## BMIS_shame_2_Z -0.009222 0.140755 -0.066 0.948
## as.factor(bias)1 -0.170631 0.290400 -0.588 0.558
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.705 on 141 degrees of freedom
## (174 observations deleted due to missingness)
## Multiple R-squared: 0.002694, Adjusted R-squared: -0.01145
## F-statistic: 0.1904 on 2 and 141 DF, p-value: 0.8268summary(lm(rl_shop_intentions ~ BMIS_shame_2_Z + as.factor(bias), wf_dt2))##
## Call:
## lm(formula = rl_shop_intentions ~ BMIS_shame_2_Z + as.factor(bias),
## data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.3104 -1.4297 0.0674 1.2768 3.5703
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.7049 0.2419 15.315 <0.0000000000000002 ***
## BMIS_shame_2_Z -0.2828 0.1716 -1.648 0.102
## as.factor(bias)1 0.3778 0.3317 1.139 0.257
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.83 on 137 degrees of freedom
## (178 observations deleted due to missingness)
## Multiple R-squared: 0.02199, Adjusted R-squared: 0.007711
## F-statistic: 1.54 on 2 and 137 DF, p-value: 0.2181summary(lm(rr_shop_intentions ~ BMIS_shame_2_Z, wf_dt2))##
## Call:
## lm(formula = rr_shop_intentions ~ BMIS_shame_2_Z, data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.6128 -0.6128 0.3872 1.3872 2.4682
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.59184 0.14197 32.345 <0.0000000000000002 ***
## BMIS_shame_2_Z -0.02601 0.13751 -0.189 0.85
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.701 on 142 degrees of freedom
## (174 observations deleted due to missingness)
## Multiple R-squared: 0.0002519, Adjusted R-squared: -0.006789
## F-statistic: 0.03578 on 1 and 142 DF, p-value: 0.8502summary(lm(rl_shop_intentions ~ BMIS_shame_2_Z, wf_dt2))##
## Call:
## lm(formula = rl_shop_intentions ~ BMIS_shame_2_Z, data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.0894 -1.4763 -0.0894 1.1335 3.5795
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.9165 0.1551 25.248 <0.0000000000000002 ***
## BMIS_shame_2_Z -0.2148 0.1611 -1.334 0.184
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.832 on 138 degrees of freedom
## (178 observations deleted due to missingness)
## Multiple R-squared: 0.01273, Adjusted R-squared: 0.005572
## F-statistic: 1.779 on 1 and 138 DF, p-value: 0.1845Shame does not seem to be related to purchase behavior at all.
summary(lm(rr_shop_intentions ~ shame_discrepancy + as.factor(bias) + defensive_reverse_Z, wf_dt2))##
## Call:
## lm(formula = rr_shop_intentions ~ shame_discrepancy + as.factor(bias) +
## defensive_reverse_Z, data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.2200 -0.6932 0.1736 1.2476 2.9549
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.6645 0.2251 20.723 <0.0000000000000002 ***
## shame_discrepancy 0.3395 0.1974 1.720 0.0878 .
## as.factor(bias)1 -0.1153 0.3275 -0.352 0.7252
## defensive_reverse_Z -0.2396 0.1552 -1.544 0.1250
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.701 on 134 degrees of freedom
## (180 observations deleted due to missingness)
## Multiple R-squared: 0.03909, Adjusted R-squared: 0.01758
## F-statistic: 1.817 on 3 and 134 DF, p-value: 0.147summary(lm(rr_shop_intentions ~ shame_discrepancy*as.factor(bias) + defensive_reverse_Z, wf_dt2))##
## Call:
## lm(formula = rr_shop_intentions ~ shame_discrepancy * as.factor(bias) +
## defensive_reverse_Z, data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.3355 -0.7036 0.1668 1.3122 2.9816
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 4.61642 0.24409 18.913
## shame_discrepancy 0.17915 0.36808 0.487
## as.factor(bias)1 -0.08099 0.33507 -0.242
## defensive_reverse_Z -0.23858 0.15566 -1.533
## shame_discrepancy:as.factor(bias)1 0.22564 0.43660 0.517
## Pr(>|t|)
## (Intercept) <0.0000000000000002 ***
## shame_discrepancy 0.627
## as.factor(bias)1 0.809
## defensive_reverse_Z 0.128
## shame_discrepancy:as.factor(bias)1 0.606
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.706 on 133 degrees of freedom
## (180 observations deleted due to missingness)
## Multiple R-squared: 0.04102, Adjusted R-squared: 0.01218
## F-statistic: 1.422 on 4 and 133 DF, p-value: 0.23summary(lm(rr_shop_intentions ~ shame_discrepancy*as.factor(bias), wf_dt2))##
## Call:
## lm(formula = rr_shop_intentions ~ shame_discrepancy * as.factor(bias),
## data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.2412 -0.7069 0.2929 1.2931 2.6647
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 4.7193 0.2282 20.678
## shame_discrepancy 0.1284 0.3475 0.369
## as.factor(bias)1 -0.2905 0.3036 -0.957
## shame_discrepancy:as.factor(bias)1 0.2820 0.4179 0.675
## Pr(>|t|)
## (Intercept) <0.0000000000000002 ***
## shame_discrepancy 0.712
## as.factor(bias)1 0.340
## shame_discrepancy:as.factor(bias)1 0.501
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.691 on 140 degrees of freedom
## (174 observations deleted due to missingness)
## Multiple R-squared: 0.02538, Adjusted R-squared: 0.004496
## F-statistic: 1.215 on 3 and 140 DF, p-value: 0.3065summary(lm(shop_intentions_Z ~ shame_discrepancy*as.factor(brand_race) + as.factor(bias), wf_dt2))##
## Call:
## lm(formula = shop_intentions_Z ~ shame_discrepancy * as.factor(brand_race) +
## as.factor(bias), data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.9118 -0.4506 0.1116 0.6016 1.5356
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.08118 0.09017 0.900 0.368725
## shame_discrepancy 0.17501 0.09376 1.867 0.062997
## as.factor(brand_race)1 0.33084 0.09814 3.371 0.000854
## as.factor(bias)1 -0.09516 0.10490 -0.907 0.365096
## shame_discrepancy:as.factor(brand_race)1 -0.03625 0.12717 -0.285 0.775803
##
## (Intercept)
## shame_discrepancy .
## as.factor(brand_race)1 ***
## as.factor(bias)1
## shame_discrepancy:as.factor(brand_race)1
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8272 on 280 degrees of freedom
## (33 observations deleted due to missingness)
## Multiple R-squared: 0.05477, Adjusted R-squared: 0.04127
## F-statistic: 4.056 on 4 and 280 DF, p-value: 0.003258summary(lm(rl_shop_intentions ~ shame_discrepancy*as.factor(bias), wf_dt2))##
## Call:
## lm(formula = rl_shop_intentions ~ shame_discrepancy * as.factor(bias),
## data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.2109 -1.8066 0.1516 1.1934 3.1934
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 3.85973 0.26336 14.656
## shame_discrepancy 0.11730 0.42810 0.274
## as.factor(bias)1 0.03559 0.34502 0.103
## shame_discrepancy:as.factor(bias)1 0.27216 0.49656 0.548
## Pr(>|t|)
## (Intercept) <0.0000000000000002 ***
## shame_discrepancy 0.784
## as.factor(bias)1 0.918
## shame_discrepancy:as.factor(bias)1 0.585
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.839 on 136 degrees of freedom
## (178 observations deleted due to missingness)
## Multiple R-squared: 0.0204, Adjusted R-squared: -0.001211
## F-statistic: 0.944 on 3 and 136 DF, p-value: 0.4214summary(lm(rr_shop_intentions ~ shame_discrepancy + as.factor(bias), wf_dt2))##
## Call:
## lm(formula = rr_shop_intentions ~ shame_discrepancy + as.factor(bias),
## data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.085 -0.746 0.254 1.254 3.010
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.7771 0.2111 22.631 <0.0000000000000002 ***
## shame_discrepancy 0.3234 0.1926 1.679 0.0954 .
## as.factor(bias)1 -0.3318 0.2967 -1.118 0.2654
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.688 on 141 degrees of freedom
## (174 observations deleted due to missingness)
## Multiple R-squared: 0.02221, Adjusted R-squared: 0.008341
## F-statistic: 1.601 on 2 and 141 DF, p-value: 0.2053summary(lm(rl_shop_intentions ~ shame_discrepancy + as.factor(bias), wf_dt2))##
## Call:
## lm(formula = rl_shop_intentions ~ shame_discrepancy + as.factor(bias),
## data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.1754 -1.8140 0.1143 1.1563 3.1563
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.918950 0.239566 16.359 <0.0000000000000002 ***
## shame_discrepancy 0.319589 0.216358 1.477 0.142
## as.factor(bias)1 -0.002518 0.337075 -0.007 0.994
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.834 on 137 degrees of freedom
## (178 observations deleted due to missingness)
## Multiple R-squared: 0.01823, Adjusted R-squared: 0.003902
## F-statistic: 1.272 on 2 and 137 DF, p-value: 0.2835summary(lm(rr_shop_intentions ~ shame_discrepancy, wf_dt2))##
## Call:
## lm(formula = rr_shop_intentions ~ shame_discrepancy, data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.1069 -0.5768 0.3243 1.4232 3.0204
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.6014 0.1410 32.633 <0.0000000000000002 ***
## shame_discrepancy 0.2554 0.1829 1.396 0.165
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.689 on 142 degrees of freedom
## (174 observations deleted due to missingness)
## Multiple R-squared: 0.01354, Adjusted R-squared: 0.006591
## F-statistic: 1.949 on 1 and 142 DF, p-value: 0.1649summary(lm(rl_shop_intentions ~ shame_discrepancy, wf_dt2))##
## Call:
## lm(formula = rl_shop_intentions ~ shame_discrepancy, data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.1760 -1.8135 0.1131 1.1550 3.1550
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.9176 0.1546 25.344 <0.0000000000000002 ***
## shame_discrepancy 0.3190 0.1992 1.601 0.112
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.827 on 138 degrees of freedom
## (178 observations deleted due to missingness)
## Multiple R-squared: 0.01823, Adjusted R-squared: 0.01112
## F-statistic: 2.563 on 1 and 138 DF, p-value: 0.1117summary(lm(rr_shop_intentions ~ bias_aware_Z + as.factor(bias), wf_dt2))##
## Call:
## lm(formula = rr_shop_intentions ~ bias_aware_Z + as.factor(bias),
## data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.6975 -0.6707 0.3025 1.3182 2.5382
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.68522 0.20652 22.687 <0.0000000000000002 ***
## bias_aware_Z 0.02618 0.15059 0.174 0.862
## as.factor(bias)1 -0.17753 0.28486 -0.623 0.534
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.704 on 141 degrees of freedom
## (174 observations deleted due to missingness)
## Multiple R-squared: 0.002877, Adjusted R-squared: -0.01127
## F-statistic: 0.2034 on 2 and 141 DF, p-value: 0.8162summary(lm(rr_shop_intentions ~ bias_aware_Z*as.factor(bias), wf_dt2))##
## Call:
## lm(formula = rr_shop_intentions ~ bias_aware_Z * as.factor(bias),
## data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.8125 -0.6776 0.1875 1.2953 2.6549
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.7138 0.2081 22.650 <0.0000000000000002
## bias_aware_Z 0.2105 0.2282 0.922 0.358
## as.factor(bias)1 -0.2116 0.2865 -0.739 0.461
## bias_aware_Z:as.factor(bias)1 -0.3261 0.3036 -1.074 0.285
##
## (Intercept) ***
## bias_aware_Z
## as.factor(bias)1
## bias_aware_Z:as.factor(bias)1
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.704 on 140 degrees of freedom
## (174 observations deleted due to missingness)
## Multiple R-squared: 0.01103, Adjusted R-squared: -0.01017
## F-statistic: 0.5203 on 3 and 140 DF, p-value: 0.669summary(lm(shop_intentions_Z ~ bias_aware_Z*as.factor(brand_race)*as.factor(bias), wf_dt2))##
## Call:
## lm(formula = shop_intentions_Z ~ bias_aware_Z * as.factor(brand_race) *
## as.factor(bias), data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.07569 -0.65587 0.09075 0.72615 1.68549
##
## Coefficients:
## Estimate Std. Error
## (Intercept) -0.14456 0.11166
## bias_aware_Z 0.06588 0.10967
## as.factor(brand_race)1 0.39754 0.15626
## as.factor(bias)1 0.16122 0.15293
## bias_aware_Z:as.factor(brand_race)1 -0.05128 0.16363
## bias_aware_Z:as.factor(bias)1 -0.06703 0.14614
## as.factor(brand_race)1:as.factor(bias)1 -0.17950 0.21560
## bias_aware_Z:as.factor(brand_race)1:as.factor(bias)1 0.01432 0.21846
## t value Pr(>|t|)
## (Intercept) -1.295 0.1964
## bias_aware_Z 0.601 0.5485
## as.factor(brand_race)1 2.544 0.0115 *
## as.factor(bias)1 1.054 0.2927
## bias_aware_Z:as.factor(brand_race)1 -0.313 0.7542
## bias_aware_Z:as.factor(bias)1 -0.459 0.6468
## as.factor(brand_race)1:as.factor(bias)1 -0.833 0.4058
## bias_aware_Z:as.factor(brand_race)1:as.factor(bias)1 0.066 0.9478
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9314 on 294 degrees of freedom
## (16 observations deleted due to missingness)
## Multiple R-squared: 0.03092, Adjusted R-squared: 0.007845
## F-statistic: 1.34 on 7 and 294 DF, p-value: 0.2311summary(lm(rl_shop_intentions ~ bias_aware_Z*as.factor(bias), wf_dt2))##
## Call:
## lm(formula = rl_shop_intentions ~ bias_aware_Z * as.factor(bias),
## data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.1311 -1.8420 0.1247 1.1378 3.2090
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.860410 0.233647 16.522 <0.0000000000000002
## bias_aware_Z -0.008493 0.228456 -0.037 0.970
## as.factor(bias)1 0.164364 0.316026 0.520 0.604
## bias_aware_Z:as.factor(bias)1 -0.091074 0.302654 -0.301 0.764
##
## (Intercept) ***
## bias_aware_Z
## as.factor(bias)1
## bias_aware_Z:as.factor(bias)1
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.856 on 137 degrees of freedom
## (177 observations deleted due to missingness)
## Multiple R-squared: 0.003581, Adjusted R-squared: -0.01824
## F-statistic: 0.1641 on 3 and 137 DF, p-value: 0.9204summary(lm(rr_shop_intentions ~ bias_aware_Z + as.factor(bias), wf_dt2))##
## Call:
## lm(formula = rr_shop_intentions ~ bias_aware_Z + as.factor(bias),
## data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.6975 -0.6707 0.3025 1.3182 2.5382
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.68522 0.20652 22.687 <0.0000000000000002 ***
## bias_aware_Z 0.02618 0.15059 0.174 0.862
## as.factor(bias)1 -0.17753 0.28486 -0.623 0.534
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.704 on 141 degrees of freedom
## (174 observations deleted due to missingness)
## Multiple R-squared: 0.002877, Adjusted R-squared: -0.01127
## F-statistic: 0.2034 on 2 and 141 DF, p-value: 0.8162summary(lm(rl_shop_intentions ~ bias_aware_Z + as.factor(bias), wf_dt2))##
## Call:
## lm(formula = rl_shop_intentions ~ bias_aware_Z + as.factor(bias),
## data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.08465 -1.79717 0.04943 1.12032 3.12163
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.86674 0.23193 16.672 <0.0000000000000002 ***
## bias_aware_Z -0.06039 0.14935 -0.404 0.687
## as.factor(bias)1 0.15340 0.31288 0.490 0.625
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.85 on 138 degrees of freedom
## (177 observations deleted due to missingness)
## Multiple R-squared: 0.002923, Adjusted R-squared: -0.01153
## F-statistic: 0.2023 on 2 and 138 DF, p-value: 0.8171summary(lm(rr_shop_intentions ~ bias_aware_Z, wf_dt2))##
## Call:
## lm(formula = rr_shop_intentions ~ bias_aware_Z, data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.6157 -0.6053 0.3930 1.4017 2.4436
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.59221 0.14244 32.239 <0.0000000000000002 ***
## bias_aware_Z 0.02043 0.14998 0.136 0.892
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.701 on 142 degrees of freedom
## (174 observations deleted due to missingness)
## Multiple R-squared: 0.0001306, Adjusted R-squared: -0.006911
## F-statistic: 0.01855 on 1 and 142 DF, p-value: 0.8918summary(lm(rl_shop_intentions ~ bias_aware_Z, wf_dt2))##
## Call:
## lm(formula = rl_shop_intentions ~ bias_aware_Z, data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.0255 -1.8395 0.0675 1.0985 3.1915
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.95052 0.15637 25.263 <0.0000000000000002 ***
## bias_aware_Z -0.06051 0.14894 -0.406 0.685
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.845 on 139 degrees of freedom
## (177 observations deleted due to missingness)
## Multiple R-squared: 0.001186, Adjusted R-squared: -0.006
## F-statistic: 0.165 on 1 and 139 DF, p-value: 0.68527. Word of Mouth
summary(lm(wom_Z ~ as.factor(bias)*as.factor(brand_race), wf_dt2)) # main interaction working, it is defensiveness driving this effect, whe defensiveness is included as a term in this model the effect vanishes, but defensiveness on its own is still negatively correlated with the dv, it also loses its sig for shame and se, but they are not signifgicant predictors of the dv##
## Call:
## lm(formula = wom_Z ~ as.factor(bias) * as.factor(brand_race),
## data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.0582 -0.7747 0.1550 0.7199 2.0588
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.4693 0.1024 -4.584 0.000006596
## as.factor(bias)1 0.3870 0.1475 2.624 0.00911
## as.factor(brand_race)1 0.7853 0.1505 5.217 0.000000331
## as.factor(bias)1:as.factor(brand_race)1 -0.4010 0.2133 -1.880 0.06099
##
## (Intercept) ***
## as.factor(bias)1 **
## as.factor(brand_race)1 ***
## as.factor(bias)1:as.factor(brand_race)1 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9494 on 314 degrees of freedom
## Multiple R-squared: 0.1072, Adjusted R-squared: 0.09868
## F-statistic: 12.57 on 3 and 314 DF, p-value: 0.00000008821summary(lm(wom_Z ~ shame_discrepancy, wf_dt2)) # shame does not predict it neither does shame change, and it doesn't interact with anything to predict it##
## Call:
## lm(formula = wom_Z ~ shame_discrepancy, data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.66782 -0.49641 -0.02045 0.46941 1.65472
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.19227 0.05057 3.802 0.000176 ***
## shame_discrepancy 0.10573 0.06551 1.614 0.107629
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8537 on 283 degrees of freedom
## (33 observations deleted due to missingness)
## Multiple R-squared: 0.009122, Adjusted R-squared: 0.00562
## F-statistic: 2.605 on 1 and 283 DF, p-value: 0.1076summary(lm(wom_Z ~ defensive_reverse_Z*as.factor(brand_race), wf_dt2)) # defensiveness does predict it##
## Call:
## lm(formula = wom_Z ~ defensive_reverse_Z * as.factor(brand_race),
## data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.92602 -0.42021 0.08609 0.53949 1.75657
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) -0.03836 0.06931 -0.553
## defensive_reverse_Z -0.15842 0.06996 -2.264
## as.factor(brand_race)1 0.46415 0.09837 4.718
## defensive_reverse_Z:as.factor(brand_race)1 0.04662 0.09855 0.473
## Pr(>|t|)
## (Intercept) 0.5804
## defensive_reverse_Z 0.0243 *
## as.factor(brand_race)1 0.0000038 ***
## defensive_reverse_Z:as.factor(brand_race)1 0.6365
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.82 on 274 degrees of freedom
## (40 observations deleted due to missingness)
## Multiple R-squared: 0.09728, Adjusted R-squared: 0.0874
## F-statistic: 9.843 on 3 and 274 DF, p-value: 0.000003472summary(lm(wom_Z ~ SE_change, wf_dt2)) # self-esteem change does not##
## Call:
## lm(formula = wom_Z ~ SE_change, data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.4919 -0.5102 -0.0340 0.4420 1.4087
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.19504 0.05073 3.844 0.000149 ***
## SE_change -0.01925 0.16637 -0.116 0.907976
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8578 on 284 degrees of freedom
## (32 observations deleted due to missingness)
## Multiple R-squared: 4.713e-05, Adjusted R-squared: -0.003474
## F-statistic: 0.01339 on 1 and 284 DF, p-value: 0.908summary(lm(wom_Z ~ bias_discrepancy, wf_dt2)) # BD does not##
## Call:
## lm(formula = wom_Z ~ bias_discrepancy, data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.52951 -0.50224 -0.02628 0.44968 1.49203
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.18792 0.05224 3.597 0.000379 ***
## bias_discrepancy -0.01507 0.02710 -0.556 0.578549
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8574 on 284 degrees of freedom
## (32 observations deleted due to missingness)
## Multiple R-squared: 0.001088, Adjusted R-squared: -0.002429
## F-statistic: 0.3093 on 1 and 284 DF, p-value: 0.5785summary(lm(wom_Z ~ bias_aware_Z*as.factor(brand_race)*as.factor(bias), wf_dt2)) # BA does not##
## Call:
## lm(formula = wom_Z ~ bias_aware_Z * as.factor(brand_race) * as.factor(bias),
## data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.1145 -0.6867 0.2219 0.7105 1.7909
##
## Coefficients:
## Estimate Std. Error
## (Intercept) -0.18449 0.11062
## bias_aware_Z 0.07858 0.10866
## as.factor(brand_race)1 0.49146 0.15482
## as.factor(bias)1 0.09996 0.15152
## bias_aware_Z:as.factor(brand_race)1 -0.15133 0.16211
## bias_aware_Z:as.factor(bias)1 -0.05385 0.14479
## as.factor(brand_race)1:as.factor(bias)1 -0.10521 0.21361
## bias_aware_Z:as.factor(brand_race)1:as.factor(bias)1 0.12039 0.21643
## t value Pr(>|t|)
## (Intercept) -1.668 0.09643 .
## bias_aware_Z 0.723 0.47015
## as.factor(brand_race)1 3.174 0.00166 **
## as.factor(bias)1 0.660 0.50995
## bias_aware_Z:as.factor(brand_race)1 -0.934 0.35133
## bias_aware_Z:as.factor(bias)1 -0.372 0.71023
## as.factor(brand_race)1:as.factor(bias)1 -0.493 0.62270
## bias_aware_Z:as.factor(brand_race)1:as.factor(bias)1 0.556 0.57847
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9228 on 294 degrees of freedom
## (16 observations deleted due to missingness)
## Multiple R-squared: 0.05836, Adjusted R-squared: 0.03594
## F-statistic: 2.603 on 7 and 294 DF, p-value: 0.01278*** Correlation Matrices ****
colnames(wf_dt2)## [1] "ParticipantID" "StartDate"
## [3] "EndDate" "Status"
## [5] "Progress" "Duration (in seconds)"
## [7] "Finished" "RecordedDate"
## [9] "ResponseId" "DistributionChannel"
## [11] "UserLanguage" "consent"
## [13] "prolificID" "BA_1"
## [15] "BA_2" "BA_3"
## [17] "BA_4" "SE1"
## [19] "BMIS_sad_1" "BMIS_shame_1"
## [21] "BMIS_guilt_1" "BMIS_tired_1"
## [23] "BMIS_nervous_1" "BMIS_calm_1"
## [25] "BMIS_fedup_1" "BMIS_loving_1"
## [27] "BMIS_angry_1" "BMIS_lively_1"
## [29] "BMIS_caring_1" "BMIS_content_1"
## [31] "BMIS_gloomy_1" "BMIS_jittery_1"
## [33] "BMIS_drowsy_1" "BMIS_happy_1"
## [35] "Q1 RP1" "Q2 RP2"
## [37] "Q3 RP3" "Q4 RP4"
## [39] "Q5 RP5" "Q6 RP6"
## [41] "Q7 RP7" "Q8 RN1"
## [43] "Q9 RN2" "Q10 RN3"
## [45] "Q11 RN4" "Q12 RN5"
## [47] "Q13 RN6" "Q14 RN7"
## [49] "Q15 LP1" "Q16 LP2"
## [51] "Q17 LP3" "Q18 LP4"
## [53] "Q19 LP5" "Q20 LP6"
## [55] "Q21 LP7" "Q22 LN1"
## [57] "Q23 LN2" "Q24 LN3"
## [59] "Q25 LN4" "Q26 LN5"
## [61] "Q27 LN6" "Q28 LN7"
## [63] "spwtime_First Click" "spwtime_Last Click"
## [65] "spwtime_Page Submit" "spwtime_Click Count"
## [67] "attentioncheck_nb" "discrepancy_nb"
## [69] "BMIS_sad_2" "BMIS_shame_2"
## [71] "BMIS_guilt_2" "BMIS_tired_2"
## [73] "BMIS_nervous_2" "BMIS_calm_2"
## [75] "BMIS_fedup_2" "BMIS_loving_2"
## [77] "BMIS_angry_2" "BMIS_lively_2"
## [79] "BMIS_caring_2" "BMIS_content_2"
## [81] "BMIS_gloomy_2" "BMIS_jittery_2"
## [83] "BMIS_drowsy_2" "BMIS_happy_2"
## [85] "credibility" "objective"
## [87] "valid" "useful"
## [89] "rl_product_choice" "rl_shop_intentions"
## [91] "rl_purchase" "rl_wom"
## [93] "rr_product_choice" "rr_shop_intentions"
## [95] "rr_purchase" "rr_wom"
## [97] "attentioncheck_b" "discrepancy_b"
## [99] "SE_2" "age"
## [101] "race" "education"
## [103] "polit_affil" "polit_affil_4_TEXT"
## [105] "polit_affil_cont_1" "gender"
## [107] "gender_4_TEXT" "iat_prev"
## [109] "iat_racial" "iat_racial_time"
## [111] "iat_racial_quant" "recent_results"
## [113] "nobias_white" "nobias_black"
## [115] "bias_white" "bias_black"
## [117] "discrepancy_bias" "discrepancy_nobias"
## [119] "bias_discrepancy" "bias_discrepancy_centered"
## [121] "bias_discrepancy_Z" "condition"
## [123] "bias" "brand_race"
## [125] "shop_intentions" "shop_intentions_centered"
## [127] "shop_intentions_Z" "purchase"
## [129] "purchase_centered" "purchase_Z"
## [131] "wom" "wom_centered"
## [133] "wom_Z" "defensive"
## [135] "defensive_reverse" "defensive_reverse_centered"
## [137] "defensive_reverse_Z" "bias_aware"
## [139] "bias_aware_center" "bias_aware_Z"
## [141] "BMIS_guilt_1_center" "BMIS_guilt_1_Z"
## [143] "BMIS_shame_1_center" "BMIS_shame_1_Z"
## [145] "BMIS_sad_1_center" "BMIS_sad_1_Z"
## [147] "BMIS_guilt_2_center" "BMIS_guilt_2_Z"
## [149] "BMIS_shame_2_center" "BMIS_shame_2_Z"
## [151] "BMIS_sad_2_center" "BMIS_sad_2_Z"
## [153] "guilt_shame_sad" "guilt_shame_sad_center"
## [155] "guilt_shame_sad_Z" "shame_discrepancy"
## [157] "SE_2_Z" "SE_1_Z"
## [159] "SE_change"# Calculate correlation between variables
cor_matrix <- wf_dt2 %>%
select(
"BMIS_shame_2_Z",
"defensive_reverse_Z",
"bias_aware_Z",
"guilt_shame_sad_Z",
"SE_2_Z",
"shop_intentions",
"wom",
"purchase",
"polit_affil_cont_1",
"rl_shop_intentions",
"rl_purchase",
"rl_wom",
"rr_shop_intentions",
"rr_purchase",
"rr_wom") %>%
cor(use = "pairwise.complete.obs")
# Create a function to calculate p-values
get_p_value <- function(x, y) {
cor_test <- cor.test(x, y, method = "pearson")
return(cor_test$p.value)
}
# Calculate p-values for the correlations
p_values <- outer(colnames(cor_matrix), colnames(cor_matrix),
Vectorize(function(x, y) get_p_value(cor_matrix[, x], cor_matrix[, y])))
# Create a dataframe for the correlation values and p-values
corr_data <- data.frame(variables = rep(colnames(cor_matrix), each = ncol(cor_matrix)),
others = rep(colnames(cor_matrix), ncol(cor_matrix)),
corr_values = as.vector(cor_matrix),
p_value = as.vector(p_values))
# Create a function to add asterisks based on p-values
add_asterisk <- function(p_value) {
if (p_value < 0.05) {
return("*")
} else {
return("")
}
}
# Apply the function to generate the asterisk labels
corr_data$asterisks <- sapply(corr_data$p_value, add_asterisk)
# Create a heatmap for the correlation matrix with labels and significance asterisks
heatmap_plot <- ggplot(data = corr_data, aes(x = variables, y = others, fill = corr_values)) +
geom_tile() +
geom_text(aes(label = sprintf("%.2f", corr_values)), color = "black", size = 3) + # Display correlation values as labels
geom_text(aes(label = asterisks), color = "red", size = 5) + # Display significance asterisks
scale_fill_gradient2(low = "blue", mid = "white", high = "red",
midpoint = 0, limits = c(-1, 1)) +
labs(title = "Correlation Heatmap",
x = "Variables",
y = "Other Variables") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
# Display the heatmap
print(heatmap_plot)
# Install and load the writexl package if not already installed
# install.packages("writexl")
library(writexl)
# Specify the file path where you want to save the Excel file
excel_file_path <- "correlation_matrix.xlsx"
# Write the correlation matrix to an Excel sheet
write_xlsx(corr_data, path = excel_file_path)
# Display the file path to confirm where the Excel file is saved
excel_file_path## [1] "correlation_matrix.xlsx"old_data <- wf_dt2